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Capital Structure Determinants of Automotive Firms: Evidence from Four
ASEAN Countries
Research · October 2018
DOI: 10.13140/RG.2.2.19831.44961
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Capital Structure Determinants of Automotive Firms: Evidence from Four
ASEAN Countries
Raymond Chia Tsun Siung1*
1
Postgraduate candidate of Faculty of Business and Accountancy, University of Malaya,
50603 Kuala Lumpur, Malaysia. Email: rtschia@gmail.com
Abstract
This paper aims to investigate the relationship between a firm’s financial
performance and its choice of capital structure on the basis of automotive firms listed on the
KLSE, SGX, SET, and IDX over the period 2001–2016. Unbalanced panel data from the
quarterly data of 66 listed automotive firms across the four countries, in order to answer the
following two questions: Are capital structures of automotive firms in the four countries
driven by firm-specific determinants similar to capital structures previously described in the
literature? Do country-specific determinants play a role in the automotive firm’s choice of
financing? We employed two different proxies for firm financial performance (return on asset
and return on equity). We include the firm-specific variables size, tangibility, liquidity,
revenue growth, and country-specific variables—these last refer to fluctuation of the national
currency against the greenback, GDP growth, inflation, and country financial depth. Our
study suggests that the ROA significantly influences the total debt ratio across the automotive
firms in the four ASEAN countries, although this is not the case with ROE, and the sign of
correlation of the other firm variables are mixed, indicating equivocal evidence supporting
both trade-off theory and pecking order theory.
Keywords: Capital Structure, ASEAN, Automotive Industry
JEL Classification Code: D01, D22, G32
1. Introduction
The automotive industry, dubbed the industry of industries, is one of the most
globalized business at present (Wad, 2009). One reason for its nickname is possibly the role it
has played in guiding individual countries’ economic development and industrialization (Tai,
2016), which can be observed even today in developing countries, where robust development
of the infrastructure landscape, such as the construction of more highways, is made possible,
leading to feasible logistic and economic activities. However, concern over the impact of the
fourth industrial revolution, as described by Wad (2009), and the increasing needs of robotic
automation, observed by the International Labour Organization (2016), are just some of the
factors calling for extensive capital expenditures. All things being equal, which source of
funding should be utilized?
1
This paper aims to investigate the capital structure determinants of listed automotive
firms in four ASEAN countries—namely Indonesia, Malaysia, Singapore, and Thailand. The
term capital structure refers to the mix of debt and equity (Brealey et al., 2014) of a firm, and
the studies that have been conducted in this field have attempted to explain the financing
decisions of firms for their real investment (Myers, 2001). In this paper, we focus on
investigating the capital structure determinants of the automotive industry in this region,
whether the commonly used determinants are applicable to this industry, and how some of
the macroeconomic key indicators of each countries affect this decision.
Firms must to be efficient if they are to stay relevant in the competitive business
world. One of the details that we had to take into the account was the choice of the mix of
capital structure, which does influence the value of a firm (Modigliani & Miller 1963, Kraus
& Litzenberger 1973). We look into the capital structure determinants of the automotive
industry by making a comparison between four countries in the ASEAN region. In terms of
the automotive industry, the ASEAN region tends to be considered as a single country. Our
comparison also allowed us to see whether automotive firms in this region behave similarly
when making the debt or equity decision.
In 2015, ASEAN was the seventh largest vehicle producer globally (International
Labour Organization, 2016), and it is likely to climb past Japan and Russia to the fifth place
by year 2020 (Frost & Sullivan, 2014). The sales and production of both commercial and
private motor vehicles in Indonesia, Malaysia, and Thailand has been consistently above the
regional average, with the exception of Singapore, which is the only ASEAN country
considered a developed nation. Numbers obtained from the ASEAN Automotive Federation
show that, in 2016, the cars produced in Thailand, Malaysia, and Indonesia represented more
than 90% of the total cars produced in ASEAN, and that over 75% of the cars sold in the
ASEAN market are sold in Thailand, Malaysia, and Indonesia. In spite a slowdown in sales
and production during 2014 and 2015, both sales and production in the four selected
countries showed resilient growth at respectively 3.1% and 3.2% in 2016, compared to 2015,
and the five-year compound annual growth rate of sales and production since 2011 were
respectively 4.1% and 6.1%. The four selected ASEAN countries are Common Law countries
(Alves & Ferreira, 2011), so we assumed the political and legal effects to be a constant
captured in the intercept.
2. Literature Review
The basic source of income for any firm is the stream of cash flows produced by its
various assets, be they tangible or intangible. If the firm is financed entirely by equity, then
all these generated cash flows belong to the stockholders, while if the firm is financed
partially by both debt and equity, then these cash flows would be split accordingly between
the bondholders and the stockholders. Research on firm capital structure originated with the
seminal paper of Modigliani & Miller (1958), who put forward the controversial proposition
that the mix of capital structure is irrelevant to the firm’s value. Their thirty-seven-page paper
suggested a total of three propositions, often known now as the capital structure irrelevance
theory, based the assumptions that the financial market is perfect; that there is no cost to
bankruptcy, no financial friction, and no taxation; that the management of the firm is always
acting in the best interest of the shareholders, and that individual investor and firm could
borrow at the same interest rate. These assumptions are important, because they are
fundamental to the rise of many subsequent capital structure theories, as one by one each of
2
these assumptions was relaxed, thus forming the building blocks of our understanding of how
firms finance their assets.
One well-known capital structure theory, trade-off theory, was created when
Modigliani & Miller (1963) lifted their assumption of no taxation, which was subsequently
furthered by Kraus & Litzenberger (1973) with the lifting of the assumption that there is no
bankruptcy cost. The ideas of trade-off theory revolve around the trade-off between the
benefit and cost of issuing additional debt, which are respectively the tax shield and financial
distress. This classical idea has since been extended with the dynamic assumption of market
imperfection, where the capital structure is adjusted in order to obtain optimum firm value.
The adoption of transaction costs, for example, produces three strongly debated research
questions (Getzmann et al., 2014) concerning the speed of adjustment, the magnitude of the
transaction cost, and the firm’s behavior in response to the capital structure shock. By lifting
the assumptions that managers always act in the best interest of the principal and that
investors and the firm can borrow at the same rate, the agency cost of debt theory was
introduced by Jensen & Meckling (1976); this later developed into the free cash flow theory
of Jensen (1986), which suggested the role of debt in mitigating the agency cost. The
fundamental idea of signaling theory (Ross, 1977) was extended to pecking order theory
(Majluf & Myers, 1984) and market timing theory (Wurgler & Baker, 2002), all of which are
based on the idea of asymmetrical information raising the problem of adverse selection and
moral hazard.
Extensive empirical investigations aimed at verifying the claims of each of these
capital structure theories have been carried over the last few decades (Kumar et al., 2017). In
earliest empirical investigations, which used company data from the G7 countries (Rajan &
Zingales, 1995) and from ten developing countries (Booth et al. 2001), four firm-specific
determinants—namely, firm size, tangibility, market-to-book ratio, and profitability—were
found to have an unequivocally significant effect on the debt ratio. The debt ratio has been
used as a proxy for the firm’s choice of capital structure. In a direct comparison between
developed and developing countries, Demirgüç-Kunt & Maksimovic (1999) confirmed
similar findings. A subsequent survey by Bancel & Mittoo (2002) however, found no
evidence for any of the capital structure theories being practiced in reality. According to
Bancel & Mittoo (2002), firms in reality seek accessibility of financing facilities, regardless
of the economic outlook and of the effect of acquiring more equity or debt on the financial
statement ratios. We have found that recent papers on capital structure have mostly focused
on the effects of country-level determinants on the variation in capital structure decision
(such as Cheng, 2014; Fan et al., 2012; Gungoraydinoglu & Oztekin, 2011; Alvesa &
Ferreira, 2011; De Jong et al., 2008; Deesomsak et al., 2004). In the econometric method, the
most commonly used static models are pooled, fixed effect, and random effect models, while
the most commonly used dynamic models are the two-stage least square model and the
generalized method of moments (Arvanitis et al., 2012).
Regression models are used here to test whether firms in their respective country
aggregates can be explained by either trade-off theory or pecking order theory. The basic
difference between these competing theories is that trade-off theory suggests the use of debt
to gain as much extra benefit from the tax shield as possible, maximizing the firm’s value
while staying at a tolerable level of distress (Ahmad & Abdullah, 2013; Abdeljawad & Mat
Nor, 2017; Kraus & Litzenberger, 1973); pecking order theory, on the other hand, suggests
the use of debt only when the firm’s internal funds are exhausted, in order to avoid adverse
3
selection problems due to asymmetrical information that could cause the firm to bear an extra
unnecessary cost of finance (Miglo, 2017; Gao & Zhu, 2015; Majluf & Myers, 1984).
We posit that there may be commonly used firm-specific determinants based on
borrowing constraints, such as collateral and enforcement of covenants as required by
funders. Because of this, we find that past capital structure studies conducted on the example
of automotive firms have been performed rather similarly to studies of the capital structure of
other industries. Rafique (2011), for example, uses the panel data of eleven automotive firms
listed on the Karachi Stock Exchange in Pakistan from 2005 to 2009 but found no significant
effect of profitability or degree of financial leverage on the debt ratio—possibly because the
high industry growth in the study period impeded firm dependency on debt. Masnoon &
Saeed (2014) used pooled data of ten automotive firms listed on the Karachi Stock Exchange
from 2008 to 2012, and found that the profitability and liquidity had a significant negative
effect on the debt-to-equity ratio, although tangibility, firm size, and earnings growth were
found to not significantly affect the debt ratio. The significant negative relationship of firm
profitability and liquidity to the firm debt ratio shown by Masnoon & Saeed (2014) exhibits
certain criteria of pecking order theory. This may due to the environment in the country
possessing a certain degree of information asymmetry, causing firms to behave in such a way
as to avoid adverse selection problems.
Automotive firms in developed countries, however, can exhibit a somewhat different
financing behavior. Pinkova (2014) uses panel data from 2006 to 2010 on the Czech
automotive industry, finding that the profitability and tangibility have significant positive
effects on the total debt ratio, while firm size and liquidity have significant negative effects.
We posit that this result arises because, in developed countries, the asymmetrical information
problem is minuscule as a result of the well-developed financial intermediation, effective
information flow, legal structure, and enforcement. We should thus be able to explain why
firms in such environments will tend to be exhibit the behavior suggested by trade-off theory,
where profitable firms and firms with high collateral value, as measured by tangibility, would
borrow more due to lower financial distress and the benefit of the extra tax shield. However,
this may not be entirely true because Pinkova (2014), in the same paper, also found that
automotive firms with lower liquidity exhibit higher debt ratios. This is not a criterion of
trade-off theory, but rather of pecking order theory.
The automotive industry is a capital-intensive industry (Wad, 2009), meaning that
automotive firms with low liquidity may not be able to meet their current liabilities. This is
why Pinkova (2014) found that lower liquidity automotive firms tend to issue additional debt
to avoid the higher adverse selection problem associated with issuing additional equity.
While the use of firm size as a proxy for the probability of default was discarded by Rajan &
Zingales (1995), it remains one of the more commonly used determinants showing significant
effect on the debt ratio over the decades. Pinkova (2014), like Rajan & Zingales (1995), has
shown that large firms tend to rely on equity, while small firms rely more on debt for external
financing, which explains why significant negative effects were found among European
automotive firms. We posit that asymmetrical information is low in the case of large firms
due to higher exposure to media attention.
As pointed out by Rajan & Zingales (1995) and in the literature review of Harris &
Raviv (1991), the consensus on leverage determinants is that leverage increases with fixed
assets, nondebt tax shield, investment opportunities and firm size, while it decreases with
volatility, advertising expenditure, bankruptcy, profitability, and uniqueness of product. Since
4
then, we have seen repeated use of these variables in studies of capital structure. This paper
thus narrows down the use of these variables to the automotive industry in four ASEAN
countries to better understanding the determinants of firm choice in the mix of their capital
structure in this region.
3. Methodology
We use unbalanced panel data from the quarterly financial statements of the listed
firms that are categorized by Thompson Reuters Eikon as belonging to the automotive
industry in the four selected ASEAN countries. The three largest automobile markets in
ASEAN (International Trade Administration 2010)—Thailand, Malaysia and Indonesia, with
the addition of Singapore—are taken in this paper to represent the automotive industry of the
region. There are a total of twenty-five automotive firms (of which twenty-four were selected
for study) listed on the Kuala Lumpur Stock Exchange, KLSE Malaysia; there are twentythree (all selected) listed on the Stock Exchange of Thailand, SET; fourteen (thirteen
selected) on the Indonesia Stock Exchange, IDX; and eight (six selected) on the Singapore
Exchange, SGX.
The firm’s quarterly financial data from 2001 to 2016 was obtained from Thompson
Reuters Eikon. Country quarterly macroeconomic and financial data over the same period
was obtained from the International Monetary Fund (IMF). The number of available firms
and the number of firms selected for this study do not tally due to data constraints.
3.1 Variable definitions
The dependent variable is the capital structure. Various measures of capital structure
have employed in past studies, including debt-to-equity ratio (Jani & Bhatt, 2015; Masnoon
& Saeed, 2014; Rafique 2011), total debt ratio, long-term debt ratio, short-term debt ratio
(Abdeljawad & Mat Nor, 2017; Mirza et al., 2017; Chaklader & Chawla, 2016; Mohsin,
2016; Adhari & Viverita, 2015; Pinkova, 2012), and quasimarket value leverage (Alves &
Ferreira, 2011; Deesomsak et al. 2004; Booth et al. 1999; Rajan & Zingales, 1995). In order
to capture the effect of both long-term debt and short-term debt in the mix of the debt–equity
decision, we here employ the total debt ratio, calculated as the ratio of total debt to total
assets (TDA)—both as book values, because of the limited availability of their market
valuations—as a proxy of the capital structure.
Firm performance is seen as being the same as firm efficiency (see Margaritis &
Psillaki, 2010), and is also equated to firm value (see Dawar, 2014; Sheikh & Wang, 2013),
depending on which of the capital structure theories is employed. We use the last of these
definitions. Authors have mostly used accounting-based ratios (Alves & Ferreira, 2011; Jong
et al., 2008), such as return on assets, return on capital, or ratio of net operating cash flow to
total asset, which are derived using information from financial statements. Some notable
measures of firm performance include Tobin’s Q ratio (Hong, 2017; Zeitun & Saleh, 2015;
Lin & Chang, 2009), which combines market and book values. Two different measures,
return on asset (ROA) and return on equity (ROE), both in book-value form, are used in this
paper to proxy firm performance so as to capture the efficiency of the firm in making use of
its resources (assets and equity) to generate financial return. The return is obtained from the
income statement, which shows earnings before interest and tax, in order to allow comparison
across countries. Interest and taxation are also external factors.
5
Tangibility is also known as asset structure. We select the ratio of the total property,
plant, and equipment to the total book value of assets reported in the firm’s balance sheet as
one of the four firm-specific control variables in line with past reports in the literature
(Abdeljawad & Nor, 2017; Mirza et al., 2017; Adhari & Viverita, 2015; Haron, 2014; Dawar,
2014; Kodongo 2014; Sheikh & Wang, 2013; Fan et al., 2012; Arvanitis et al., 2012,
Gungoraydinoglu & Oztekin, 2011; Margaritis & Psillaki, 2010; Pandey, 2001). Tangibility
can be seen as the ability to serve as collateral when the firm is applying for debt (Pandey,
2001). A high tangibility could indicate lower financial risk for the lender and a control in the
case of bankruptcy (Gungoraydinoglu & Oztekin 2011). Making use of this, a firm could
borrow at higher debt ratio at lower cost. In brief, past findings have exhibited mixed
results—for example, Sheikh & Wang (2013) and Kodongo et al. (2014) have shown a
negative correlation in Pakistan and Kenya, respectively; while Arvanitis et al. (2012) and
Pandey (2001) have shown there to be a positive correlation between the tangibility and debt
ratio of European and Malaysian firms, respectively.
The firm size used in this study is the natural logarithm of the firm’s total assets. Past
reports have suggested that larger firms are usually more diversified and have an established
and stable cash flow; for this reason, larger firms can easily exhibit a higher debt ratio than
smaller firms. We posit that this is due to the fact that the funds supplier—be it a bank or the
capital market—would tend to be relatively at ease and impose lower financial friction when
lending to such larger firms. In spite of this, literature reports evidently show mixed evidence
that this is indeed the case. For example, positive correlation between firm size and leverage
was found by Abdeljawad & Nor (2017) in Malaysia, Chancharat (2015) in Thailand,
Kodongo (2015) in Kenya, Yazdanfar & Ohman (2015) in Swedish SMEs, Zeitun & Saleh
(2015) in the GCC countries, and Sheikh & Wang (2011) in Pakistan; on the other hand,
Hong (2017) found a negative correlation between firm size and leverage in South Korea.
Mirza et al. (2017) found mixed evidence using a different definition of capital structure for
firms in China.
Liquidity plays an important role in the automotive industry, as in all manufacturing
firms. A manufacturing firm without liquidity might opt to cease operation, whether
temporarily or permanently. The use of liquidity in this paper is measured using the ratio of
operating cash flow to the total current liabilities. We posit that the use of operating cash flow
is better than the conventional use of current asset to measure liquidity. A high inventory
turnover, for example, does not necessarily mean that the manufacturer would have enough
cash or margin to fulfil its current obligations: the worst case scenario is not being able to
fulfil its long term obligations, whether this consists of equity or long-term debt, due to
pricing strategies such as offering lower-end products with much smaller profit margins than
the manufacturer’s premium product. In the literature, Dawar (2014) uses a similar
measure—cash to current liabilities—and found a positive relationship between it and the
capital structure of Indian firms; Hong (2017) uses the same liquidity measure for his study in
South Korea firms, while Mirza et al. (2017) found a mixed relationship of liquidity in China
firms, depending on the definition of capital structure.
Authors have different views on what proxies best measure firm growth: for example,
Rajan & Zingales (1995) and Abdeljawad & Nor (2017) use the market-to-book ratio to
capture future growth opportunity. This paper, however, uses the quarterly growth of revenue
as a proxy for firm growth. We posit that a firm’s growth should be straightforwardly
observable in the financial statements, and not bound by market expectations of the firm, as
thus is captured in the market-to-book ratio. Dawar (2014) and Chancharat (2015) found no
6
significance relationship between firm growth and the capital structure of Indian and Thai
firms, respectively; whereas Ahmad & Abdullah (2013), Zeitun & Saleh (2015), Kodongo
(2014), Sheikh & Wang (2013), and Hong (2017) found positive relationships in Malaysia,
GCC countries, Kenya, Pakistan, and South Korea, respectively.
Fluctuation in a national currency can affect the pricing of products in the automotive
industry, as seen in recent years: as the Malaysian currency has weakened, firms in its
automotive industry have had to increase prices. This is probably due to the nature of the
automotive industry in Malaysia, which is a net import industry with the majority of firm
costs being denominated in foreign currency. For example, research done by a Malaysian
financial service company, Kenanga Research and reported on the Malaysian news portal The
Star Business in 2015, reveal that Tan Chong Motor Holding’s bottom line is said to be
affected by 6% for every 1% fluctuation in the Ringgit–USD rate, while UMW’s bottom line
could be affected by 3%. But does this variable affect a net export country such as Thailand?
Intuitively, it could have an indirect effect on firm profitability (see Jong et al., 2008;
Gungoraydinoglu & Öztekin, 2011 for their definition of direct and indirect effects of
country-specific determinants). We here use the quarterly growth rate of the currency
exchange rate provided by the IMF metadata, by country. We take the US dollar as an anchor
currency due to the easily available historical data provided by the IMF metadata.
Gross domestic product (GDP) is measured here using the quarterly growth of the
country’s gross domestic product. The values of GDP, nominal, and domestic currency are
taken from the IMF metadata for each country; overall, thus data could be seen as an
indicator of the economic health of the country (Mirza et al., 2017), and it could strengthen
the growth of the cyclically sensitive automotive industry. We would expect firms to be
willingly to borrow at higher debt ratios during boom periods in order to increase their
capacity and capture more market share, and to borrow at a lower debt ratio during bust
periods. However, we use a long fifteen-year period in this study, and so we suspect that the
effect of the GDP on firm’s behavior could be averaged out. We follow the common use of
GDP as a control variable (Haron, 2014; Alves & Ferreira, 2011; Jong et al., 2008).
Inflation is important determinant of capital structure (Alves & Ferreira, 2011) which
may affect the debt ratio through the interest rate. Central banks often adjust their monetary
policy by targeting inflation. Referring to the so-called term structure expectation hypothesis
(Mishkin 2016, pp. 172), all things being equal, at a repeatedly high short-term interest rate,
we could expect the long-term interest rate to also be equally high, and vice versa. At higher
inflation (or expected future higher interest rate), this provides motivation for firms to borrow
less long-term debt (bonds). An alternative view is that higher inflation decreases the benefit
of leverage because of the higher bankruptcy costs of debt imposed on firms
(Gungoraydinoglu & Oztekin 2011). In the case of higher inflation rates, firms choose more
short-term debts over equity (Abzari et al., 2012). We make use of the quarterly growth rate
of the consumer prices index provided by the IMF metadata by country as measure of
inflation growth in this paper.
There are four ways to measure the financial development of a country: financial
depth, accessibility, efficiency, and stability (Cihak et al., 2012). We could expect the debt
ratio to differ between countries with different development of the depth of financial
institutions. For example, Alves & Ferreira (2011), assert that a firm might have an incentive
to choose bank debt over equity on account of the higher rate of return required by
shareholders in a country with an illiquid or underdeveloped capital market. This could cause
7
a greater preference for debt by firms in such countries. One way to measure the financial
depth of the country is to use bank size, and this paper uses the definition of financial depth
from World Bank data, which is the total size of claims of domestic banks on the domestic
private sector as a percentage of the country’s GDP. This is provided by the IMF metadata
for each country.
3.2 Empirical model and descriptive statistics
The primary motivation for using the panel data is the ability to control for
unobservable heterogeneity (Arvanitis et al., 2013; Dougherty, 2011). This is because, if the
explanatory variables in our regression model are comprehensive, this will capture all the
relevant characteristics of the population, meaning that there would not be any unobservable
characteristics. If we were to leave unobserved effects untreated (even if they do not correlate
with any of the explanatory variables), their presence alone will generally cause the ordinary
least squares method to give an inefficient estimate and an invalid standard error (Dougherty,
2011).
We thus employ a fixed effect model with a robust standard error estimated using the
least squares method so as study the impact of firm performances on the choice of capital
structure mix. Our unbalanced panel data is yi,t = β0 + Ʃ(j=1 to k)βjxi,j,t + μi + εi,t, where β0
is the intercept; βjxi,j,t is all the explanatory variables we have included, and k is 9 in both
Equation 1 and 2; μi is the unobserved effect; and εi,t is the disturbance.
We use the Breusch–Pagan Lagrangian Multiplier, BPLM test to decide whether the
pooled ordinary least square estimation can be applied. Its null hypothesis implies
homoscedasticity, where Var(μi) = 0, and thus the rejection of the LM test null hypotheses
shows the existence of heterogeneity in the variables in our panel data. From the BPLM test
result, the application of the pooled least squares estimation is consistently rejected due to
violation of the least squares assumption in favor of random effect. The Hausman test was
then perform to identify whether there is a correlation between an unobservable heterogeneity
with the variables employed in our regression model. EViews was used to compute both the
random effect and fixed effect so as to compare the estimates of coefficient, Var(βRandom–
βFixed). The rejection of the null hypotheses favors the selection of a fixed effect instead of a
random effect. From our Hausman test result, the fixed effect model is accepted when the pvalue of the Hausman test is < 0.05, while the random effect model is accepted when the pvalue is > 0.05.
We then further employ the coefficient-of-covariance method for the White crosssection with robust standard error on account of our concern over heteroscedasticity, given
our small sample size. According to Cameron & Miller (2015), failure to control for withincluster error correlations can lead to very misleadingly small standard errors, and
consequently to a misleading narrow confidence interval, large t-statistic, and low p-values.
The specification with both firm-specific and country-specific determinants is shown here in
Equations 1 and 2.
TDAi,t = β0 + β1ROAi,t + β2TANi,t + β3RGRi,t + β4LIQi,t + β5SIZi,t + β6FXFj,t + β7GDPj,t +
β8INFj,t + β9BANj,t … Equation 1
TDAi,t = β0 + β1ROEi,t + β2TANi,t + β3RGRi,t + β4LIQi,t + β5SIZi,t + β6FXFj,t + β7GDPj,t +
β8INFj,t + β9BANj,t … Equation 2
8
where TDAi,t is the total debt ratio, ROAi,t is the ratio of earnings before interest and tax to the
total assets, and ROEi,t is the ratio of earnings before interest and tax to the total equity;
TANi,t is the ratio of net fixed asset to total asset; RGRi,t is the revenue growth; LIQi,t is the
ratio of operating free cash flow to the current liabilities; SIZi,t is the natural logarithm of the
total assets; FXFj,t is the fluctuation of the currency against the US dollar; GDPj,t is the gross
domestic product growth; INFi,t is the quarterly growth of the consumer price index; BANj,t is
the banking depth as a percentage of the gross domestic product; the subscripts i, j, and t
indicate firm i in country j at time t.
Table 1: Descriptive statistic of firm- and country-specific variables
Malaysia
Singapore
Thailand
Indonesia
N 1132
N 223
N 1010
N 637
μ 0.163
μ 0.252
μ 0.269
μ 0.307
Total debt ratio = total
TDA
ñ 0.108
ñ 0.230
ñ 0.260
ñ 0.294
debt / total asset
ơ 0.155
ơ 0.128
ơ 0.230
ơ 0.197
Return-on-asset =
μ 0.018
μ 0.013
μ 0.018
μ 0.017
ROA
earnings before interest
ñ 0.016
ñ 0.013
ñ 0.017
ñ 0.017
and tax / total asset
ơ 0.022
ơ 0.019
ơ 0.022
ơ 0.019
Return-on-equity =
μ 0.031
μ 0.051
μ 0.034
μ 0.043
ROE
earnings before interest
ñ 0.027
ñ 0.042
ñ 0.033
ñ 0.040
and tax / total equity
ơ 0.035
ơ 0.069
ơ 0.062
ơ 0.068
μ 0.312
μ 0.226
μ 0.451
μ 0.343
Tangibility = net fixed
TAN
ñ 0.288
ñ 0.235
ñ 0.451
ñ 0.354
asset / total asset
ơ 0.707
ơ 0.072
ơ 0.133
ơ 0.190
μ 0.021
μ 0.018
μ 0.023
μ 0.028
RGR
Growth = Δ(revenue)t,t-1
ñ 0.016
ñ 0.013
ñ 0.016
ñ 0.033
ơ 0.233
ơ 0.179
ơ 0.180
ơ 0.206
Liquidity = operating free
μ 0.201
μ 0.063
μ 0.252
μ 0.096
LIQ
cash flow / current
ñ 0.071
ñ 0.063
ñ 0.157
ñ 0.083
liabilities
ơ 0.519
ơ 0.104
ơ 0.377
ơ 0.179
μ 2.678
μ 3.172
μ 3.452
μ 5.359
Size = natural logarithm
SIZ
ñ 2.476
ñ 3.212
ñ 3.452
ñ 5.771
of total asset
ơ 0.710
ơ 0.663
ơ 0.452
ơ 1.911
Currency fluctuation
μ 0.004
μ -0.002
μ -0.002
μ 0.007
FXF
against US dollar =
ñ 0.000
ñ -0.007
ñ -0.006
ñ 0.001
Δ(currency)t,t-1
ơ 0.032
ơ 0.024
ơ 0.025
ơ 0.041
μ 0.022
μ 0.012
μ 0.016
μ 0.038
Gross domestic product
GDP
ñ 0.027
ñ 0.008
ñ 0.013
ñ 0.015
= Δ(GDP)t,t-1
ơ 0.038
ơ 0.029
ơ 0.037
ơ 0.035
μ 0.006
μ 0.005
μ 0.006
μ 0.016
Inflation = Δ(consumer
INF
ñ 0.006
ñ 0.004
ñ 0.005
ñ 0.015
price)t,t-1
ơ 0.008
ơ 0.008
ơ 0.010
ơ 0.015
Banking depth =
μ 4.384
μ 4.285
μ 3.896
μ 1.039
BAN
ratio of domestic credit /
ñ 4.390
ñ 4.149
ñ 3.752
ñ 0.960
GDP
ơ 0.369
ơ 0.649
ơ 0.450
ơ 0.157
Note for Table 1: Notation μ is the mean, ñ is the median, ơ is the standard deviation, N is the number of
observations in Malaysia. The descriptive statistic is calculated using Eviews 9 with the raw data obtained from
IMF.
Variable
Description
9
4. Results and Discussion
Table 2 present the least squares estimation results of the fixed effect model.
Table 2: Summary of regression result using both firm- and country-specific variables shown
in equation 1 and 2
Malaysia
-0.15***
-0.12
(-3.26)
(-4.23)
-0.34***
(-2.84)
0.05
(0.66)
0.32***
0.33***
(13.22)
(13.56)
0.01
-0.003
(0.52)
(-0.33)
-0.01**
-0.01***
(-2.08)
(-2.90)
0.15***
0.16***
(10.68)
(11.07)
-0.16***
-0.15***
(-3.20)
(-3.36)
-0.07
-0.07
(-1.45)
(-1.36)
-0.49**
-0.49**
(-2.38)
(-2.41)
-0.05***
-0.05***
(-8.57)
(-9.28)
1,137
1,137
***
C
ROA
ROE
TAN
RGR
LIQ
SIZ
FXF
GDP
INF
BAN
N
Singapore
-0.21***
-0.20**
(-2.37)
(-2.70)
-0.38*
(-1.87)
-0.07
(-1.17)
-0.58***
-0.58***
(-10.26)
(-10.97)
-0.04
-0.02
(-1.49)
(-1.22)
-0.05
-0.05
(-1.24)
(-1.13)
0.23***
0.23***
(6.84)
(7.27)
0.08
0.08
(0.73)
(0.63)
-0.11
-0.11
(-0.94)
(-0.91)
-2.22***
-2.14***
(-4.96)
(-4.83)
-0.03***
-0.03***
(-3.43)
(-3.14)
223
224
Thailand
0.26***
0.32
(2.81)
(2.91)
-0.18
(-0.74)
-0.01
(-0.09)
0.50***
0.51***
(11.36)
(10.72)
0.004
0.003
(0.14)
(0.13)
-0.06***
-0.04***
(-4.01)
(-3.08)
-0.03
-0.01
(-0.83)
(-0.38)
0.29*
0.31**
(1.84)
(2.04)
0.08
0.04
(0.70)
(0.37)
0.10
0.05
(0.33)
(0.20)
-0.04***
-0.04***
(-4.11)
(-4.62)
1,018
1,020
***
Indonesia
0.23**
0.28
(2.76)
(2.45)
-1.22***
(-5.16)
-0.01
(-0.13)
-0.06
-0.04
(-1.06)
(-0.65)
-0.02
-0.03
(-1.07)
(1.45)
-0.01
-0.01
(-0.37)
(-0.34)
0.10***
0.10***
(3.76)
(3.91)
0.17**
0.14*
(2.38)
(1.87)
-0.34***
-0.29***
(-4.14)
(-3.05)
-0.13
0.02
(-1.06)
(0.14)
-0.44***
-0.41***
(-9.07)
(-8.78)
647
653
***
Coefficient with White cross section standard error shown in bracket. Significance at *** 1%, ** 5% and *
10%.
4.1 Profitability and debt ratio
Using Equation 1 and 2, we found that, other than for Thailand automotive firms, the
return on assets (ROA) is significantly negatively correlated with the total debt ratio (TDA).
Automotive firms from Indonesia, Malaysia, and Singapore thus seems to exhibit the
behavior predicted by pecking order theory, with an inverse relationship between profitability
and debt usage. The relationship between profitability and debt ratio is consistently as
predicted by pecking order theory, implying that profitable automotive firms in these
countries would tend to use less debt when they are profitable since they want to avoid the
adverse selection problem that arise from asymmetrical information.
Automotive firms in Thailand, on the other hand, show no significant negative
correlation between the profitability and the total debt ratio. The greatest difference we see in
Thailand’s automotive industry is the high volume of production for export, indicating that
automotive firms in Thailand act as regional manufacturing hubs. In this capacity, Thai
automotive policy is oriented toward foreign direct investment from multinational
corporations.
10
4.2 Tangibility and debt ratio
The tangibility of the automotive firms in the four countries shows results mixed
between trade-off theory and pecking order theory. Hypothetically, we should be able to
attribute higher tangibility to higher debt ratios, because tangible assets are easier to
collateralize. We find this is true for the automotive firms in Malaysia and Thailand, where
higher tangibility significantly affecting the higher debt ratio at the 1% significance level. In
Malaysia for example, the consolidation of the banking sector in order to control the banks
competition and the introduction of the policy that good quality collateral—generally in the
form of commercial or residential real estate—be required from less well known businesses
(Sufian & Habibullah, 2013). This kind of friction needed to be overcome with the use of
tangible collateral. Trade-off theory suggests that profitable firms with high tangibility should
have lower financial distress and lower bankruptcy costs (Gungoraydinoglu & Öztekin,
2011), and should borrow at higher debt ratios in order to gain extra benefit from the tax
shield at the optimum debt ratio.
We find this is somewhat different for the automotive firms in Singapore, where we
suspect due to the capital funding and liquidity of banks are considered as key strength. The
financial friction seems to be less than in the other countries. Although pecking order theory
suggests that unprofitable firms that have exhausted their retained earnings would need to sell
off some of their fixed assets (because they would prefer to avoid external funding as much
as possible), we doubt that this happens in Singapore, because the automotive firms did not
necessarily prefer to avoid bank borrowing in this case. In Indonesia, no significant
correlation could be seen between liquidity and debt, although the sign of correlation is
negative, as in Singapore.
4.3 Firm size and debt ratio
In Singapore, the finding that firm size positively matters contradicts the finding that
tangibility negatively matters. The only possible explanation of this finding is provided by
trade-off theory. Firm size is found to be positively significant at 1% in all the four selected
ASEAN countries, indicating that larger firms tend to exhibit higher debt ratios than smaller
firms, as they have better economies of scale, diversification, and more information available,
lowering the cost of bankruptcy and financial distress. Financial friction lessens and they are
able to borrow more debt in order to gain higher tax shield benefits.
4.4 Growth and debt ratio
Hypothetically, following the agency cost of debt theory or trade-off theory, higher
revenue growth could cause a firm to incur more debt, while according to pecking order
theory, higher growth could cause a firm to incur less debt. Higher growth opportunities
indicate higher agency costs of debt, but lower agency costs of equity, leading to lower
leverage, according to the agency view of trade-off theory (Gungoraydinoglu & Oztekin,
2011). Higher consistent growth would lead firms to expand their capacity, for which debt
would a viable choice. Firms whose sales grow rapidly often need to expand their fixed assets
(Pandey, 2001). We should thus expect a positive correlation between growth and debt. On
the other hand, an alternative argument is that higher-growth firms tend to be smaller firms
that also face higher distress. Growth would then be negatively correlated with debt, as more
distressed firm would face higher friction when they needed to borrow.
11
However, this paper has shown no significant influence of realized revenue growth on
the debt ratio in automotive firms in the four selected ASEAN countries. We posit that this is
probably due to the measurement of this variable, indicating that the realized growth is not
able to influence the automotive firms’ need for debt. Intuitively, durable goods
manufacturers would not make any policies, such as debt policy, on the basis of variables that
are sensitive to cycles, such as the realized revenue growth. We would suggest a
measurement such as the market-to-book ratio, which is believed to proxy for the opportunity
of growth.
4.5 Liquidity and debt ratio
Higher liquidity could lead firm financing behavior, as trade-off theory suggests that
liquid firms incur higher debt to take advantage of the reduced financial distress and to gain
as much tax shield benefit as possible, maximizing firm value. Pecking order theory, on the
other hand, would suggest using retained earnings and placing their excess retained earnings
in safe short-term securities for cushioning the bad times. In Malaysia and Thailand, we see a
significant negative correlation between firm liquidity and the debt ratio, indicating that firms
may behave as predicted by the pecking order theory. Singapore and Indonesia, however,
showed insignificant but similarly negative correlations between firm liquidity and debt.
4.6 Country-specific determinants and debt ratio
The fluctuation of the local currency against the US dollar is seen to be a significant
positive influence on the debt ratio in Thailand and Indonesia. When the currency of a
country has a positive fluctuation, the currency is weakening (ΔFXt,t-1 where FX is the
respective national currency rate against US dollar), and this leads firms to obtain more funds
from banks in form of debt. In Malaysia, however, we showed a significant negative
correlation with the total debt ratio, indicating that Malaysian automotive firm incur more
debt when the Malaysian Ringgit is strong against the US dollar. For Singaporean automotive
firms, there was no significant effect of national currency fluctuation on firms’ capital
structure decisions.
There is a significant negative influence of the country’s gross domestic product on
the debt ratio of Indonesian firms. This indicates that automotive firms would obtain more
external funds in form of debt to continue operations when the country’s economic activities
are declining. We suspect that this is due to the automotive firms in Indonesia being able to
cushion themselves against external factors using their own country’s domestic consumption.
Thus, during economic downturns, the reduction in domestic consumption would trail behind
the reduction in operating cash flow. At this time, firms would need external funds for
working capital. However, we found no significant relationship between the country’s GDP
and the debt ratio in the cases of Malaysia, Singapore, and Thailand. Borrowing more debt in
the long term is preferred when inflation is consistently low for a few periods, because future
inflation could be expected to be low when short-term inflation is consistently low. If
monetary policy is adjusted automatically, low inflation means low future interest rates.
Although we see this to be true in Malaysia and Singapore, we could not find any significant
relationship between the inflation and debt ratio of automotive firms in Indonesia or
Thailand. Lower inflation is significantly correlated with automotive firms in Malaysia and
Singapore borrowing more.
12
Alves & Ferreira (2011) found that the bank claim on the private sector varies
positively or negatively depending on the year in their cross-sectional data. On the other
hand, we found that the bank domestic claim on the private sector in the countries studied
here has a significant negative influence on the automotive firms’ debt ratio, using our panel
data. This may indicates that when the banks had smaller claims on the domestic sector
(lower lending, lower financial depth in the country), the total debt ratio of the automotive
firms increased. The use of this measure is that it can proxy the financial depth of the
country, and thus the negative relationship indicates that the country’s underdeveloped
financial depth causes firms to rely more on the use of debt.
5. Conclusion
This paper examines both the firm-specific and country-specific determinants of the
capital structure of listed automotive firms in four selected ASEAN countries—Indonesia,
Malaysia, Singapore, and Thailand—from 2001 to 2016. The initial intuition guiding this
study was that the capital structure decision of automotive firms cannot be seen as the sole
outcome of their idiosyncratic firm characteristics, but also as a reaction to the environment
the business is operating in. We found the effects of the determinants varied between the
countries. For example, the country’s GDP was found to significantly affect automotive
firms’ total debt ratio in Indonesia, but not in the other countries, and the fluctuation in the
national currency against the greenback was found to significantly affect automotive firm’s
total debt ratio in Malaysia, Thailand, and Indonesia, though not in Singapore. One countryspecific determinant that was consistent exhibited in the four countries was country financial
depth, which indicates the significant role of the financial institutions as intermediaries when
automotive firms seek external funds.
The relationships we have observed between the country-specific and firm-specific
determinants of capital structure provide support for the applicability of pecking order theory
and trade-off theory in these ASEAN automotive firms, even though some of the data was
obtained from firms located within developed countries. Based on our empirical results, we
are unable to decisively favor either theory over the other in explaining the financing
behavior of automotive firms in the selected ASEAN countries. For example, the negative
relationship between firm profitability and the liquidity-to-total-debt ratio follows the
predictions of pecking order theory. The positive relationship between firm size and total debt
ratio, on the other hand, follows the predictions of trade-off theory. The significant effect of
tangibility differ between the studied countries. The automotive firms from Malaysia and
Thailand showed positive relationships between tangibility and total debt ratio, Singapore
showed a negative relationship, and Indonesia did not show any significant relationship.
The interaction of each variable with the debt ratio has probably not been fully
elucidated here. For example, the nature of the business, such as low liquidity required,
causes automotive firms to borrow more, with the debt contract or the negotiated terms and
conditions being imposed by the bank. In developing countries, where banks play a more
focused role as financial intermediaries, an alternative reason for firm profitability being
consistently negatively correlated with the debt ratio might be better explained in terms of the
negotiated debt contract. We propose that more qualitative studies be performed, not only on
nonfinancial firms, but also on financial institutions, paying special regard to their lending
criteria.
13
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