DETERMINANTS OF CORPORATE
BORROWINGS IN MALAYSIA
BY
TAN YI HONG
DEPARTMENT OF ECONOMICS AND CORPORATE
ADMINISTRATION
FACULTY OF ACCOUNTANCY, FINANCE AND BUSINESS
TUNKU ABDUL RAHMAN UNIVERSITY OF
MANAGEMENT AND TECHNOLOGY
KUALA LUMPUR
2023/2024
Determinants of Corporate Borrowings in Malaysia
by
Tan Yi Hong
Supervisor:Mr.Kon Yee Qin
Master of Economics, University of Malaya
Project submitted in partial fulfilment of the requirements for the award of the
Bachelor of Economics (Honours)
Department of Economics and Corporate Administration
Faculty of Accountancy, Finance and Business
Tunku Abdul Rahman University College
Kuala Lumpur
2023/2024
Copyright © 2023 by Tunku Abdul Rahman University of Management and Technology.
All rights reserved. No part of this report may be reproduced, stored in a retrieval system,
or transmitted in any form or by any means without the prior permission of Tunku Abdul
Rahman University of Management and Technology.
DECLARATION
“The project submitted herewith is a result of my own investigations. All information that
has been obtained from other sources had been fully acknowledged. I understand that
plagiarism constitutes a breach of university rules and regulations and would be subjected to
disciplinary actions.”
Name
Student ID
Signature
Tan Yi Hong
20WBR11934
Tan
Date: 10/5/2023
i
BBBE3043 PROJECT PAPER I
FEEDBACK FORM
Course: BBBE3043
Name: Tan Yi Hong
ID Number: 20WBR11934
Programme: BACHELOR OF ECONOMICS (HONS.) - REC
Year 3 Semester 2
Academic Year: 2022/2023
Title: Determinants of Corporate Borrowings ib
Supervisor:
Assessment Criteria and Comments
Marks Awarded
Introduction, articulation of research problem and objectives
Comment: Motivation of the study remains unclear. In the sub-section 2, student merely
explained the relationship of the variables based on past studies and failed to
identify the research gap. The study did not make clear on its contribution.
Literature Review
Comment: Student should re-organize the way of writing for theoretical framework
section. Student should explain the theories in different sub-sections to
make the theories easy to understand.
Methodology
Comment:
Part of the methodology was verbose and student should re-check on the
hypothesis of the test as well as certain diagnostic test for panel data
analysis.
Total Marks
/30
/35
/35
/100
Overall comments on the Project Paper:
Revision needed.
ii
Turnitin Results
iii
ACKNOWLEDGEMENT
First of all, I would like to express my deepest gratitude and appreciation to Mr Kon Yee Qin
for his guidance and supervision in my Final Year Project. I am very grateful for every feedback
that gave by my supervisor that encouraged me to put more effort and work harder to
accomplish my final year project. Mr Kon Yee Qin guidance in my final year project provided
me with greater enthusiasm and motivation to complete the final year project on time.
Next, I would like to express my thanks to Tunku Abdul Rahman University of Management
and Technology for providing its facilities such as online database to search for journals or
research papers to conduct my research. This help reduce my burden as a student in searching
research papers.
Finally, I would like to give thanks to my family for their great support and encouragement
given to me while in the process to complete this project. Through their strong support, I get
more confidence to do my project.
iv
Table of Contents
CHAPTER 1: RESEARCH INTRODUCTION ..............................................................1-15
1.1 Introduction .................................................................................................................1-4
1.2 Research Background .................................................................................................5-7
1.3 Problem Statement ....................................................................................................8-13
1.4 Research Questions ...................................................................................................... 14
1.5 Research Objectives ..................................................................................................... 14
1.5.1 General Objectives ................................................................................................ 14
1.5.2 Specific Objectives ................................................................................................. 14
1.6 Significance of study..................................................................................................... 15
CHAPTER 2: LITERATURE REVIEW .......................................................................16-44
2.1 Introduction .................................................................................................................. 16
2.2 Theoretical Framework ..........................................................................................16-20
2.3 Review on the previous literatures ........................................................................21-44
2.3.1 Tangibility and leverage ...................................................................................21-26
2.3.2 Size and leverage ...............................................................................................26-32
2.3.3 Growth and leverage ........................................................................................32-38
2.3.4 Profitability and leverage .................................................................................38-44
CHAPTER 3: METHODOLOGY ..................................................................................45-62
3.1 Introduction .................................................................................................................. 45
3.2 Economic Model Specification ...............................................................................45-46
3.3 Data, Measurement, Sources, Expected Sign and Descriptive Statistic .............47-52
3.3.1 Data, Measurement and Sources .....................................................................47-48
3.3.2 Expected Sign ......................................................................................................... 48
3.3.2.1 Tangibility ................................................................................................... 48
3.3.2.2 Size ............................................................................................................... 48
3.3.2.3 Growth ......................................................................................................... 50
3.3.2.4 Profitability .................................................................................................. 51
3.3.3 Descriptive Statistics.........................................................................................51-52
3.4 Econometric Model ...................................................................................................... 53
3.4.1 Panel Regression .................................................................................................... 53
3.4.1.1 Pooled Ordinary Least Square (OLS) .................................................53-55
3.4.1.2 Fixed Effects .............................................................................................. 55
3.4.1.3 Random Effects .....................................................................................56-57
3.4.2 Diagnostic checking ............................................................................................... 57
v
3.4.2.1 Jarque Bera (JB) test ............................................................................. 58
3.4.2.2 Serial Correlation LM test ...............................................................58-59
3.4.2.3 Heteroscedasticity test ........................................................................59-60
3.4.2.4 Cumulative Sum (CUSUM) test ........................................................60-61
3.4.2.5. CUSUM of Squares (CUSUMSQ) test ................................................... 61
3.5 Hypothesis of study .................................................................................................61-62
References ..........................................................................................................................63-66
vi
CHAPTER 1: RESEARCH INTRODUCTION
In the introduction 1.1 Introduction
part, you should
briefly explain corporate
Corporate borrowing can be defined as different choices made by companies to attain
borrowing, why it is
funds cover for their assets (Shah & Hijazi, 2004). According to Goh, Tai, Rasli, Tan, and
important to a
company, and what
you want to do in Zakuan (2018), corporate borrowings referred as decision made by the company management
your paper.
in long run to ensure value of the company reached its optimum. According to Isa (2008),
corporate borrowings mean decision relating to finance that made by businesses. Corporate
borrowings consist of 3 categories that include investment, finance and dividend (Isa, 2008).
spacing
According to Mustapha, Ismail and Minai(2011), corporate borrowings are a crucial factor that
determine success or failure of companies. Corporate borrowings can determine whether
suitable in terms of?
growth of the company is sustainable or not (Mustapha et al, 2011). The selection made for
corporate borrowings is uneasy because when company choose wrong type of corporate
borrowings, company may suffer serious financial problems and have tendency to become
insolvency (Mustapha et al, 2011). Some examples of corporate borrowing are bonds, loans
from banks, lease financing and so on (Shah & Hijazi, 2004).
Malaysia lending rates
10
9
8
7
6
5
8.6
7.7
7.1
6.5 6.3
6 6
6.5 6.4
6.1
5.1 5 4.9 4.8
4.6 4.6 4.6
4.5 4.6
4.9 4.9
4
3
2
1
0
lending rates
Figure 1: Malaysia lending rate from year 1997 to 2021(World Bank 2023).
1
3.9
3.4
CHAPTER 1: RESEARCH INTRODUCTION
I couldn't figure
out why lending
rate need to be
mentioned here.
It is not one of
your independent
variables.
Figure 1 above showed Malaysia lending rates from year 1997 to 2021. Lending rate is a cost
of borrowings for firms. The reason is because firms need to pay the lending rates inclusive
borrowings amount when they intend to borrow monies. According to Figure 1, the top lending
rates is in year 1999 with 8.6%. This will be disadvantage for firms that borrow in year 1999
because they required to pay a higher lending rate to attain their borrowings. The lowest lending
rates occurred in year 2021 with an amount of 3.4%, which was a fall of 0.5% lending rates as
compared to year 2020. Firms that applied borrowings in year 2020 and 2021 was benefited
because they can attain borrowings with a much lower lending rate. Overall, Malaysia lending
rates was showing a fluctuation trend. For evidence, referring to Figure 1, firstly, the lending
rates dropped from 8.6% in year 1999 to 6.3% in year 2003. In year 2004 and 2005, the lending
rates was kept at 6% then rose to 6.5% in year 2006. However, the lending rates commenced
fall again from 6.5% in 2006 to 4.5% in 2016. Then, the lending rates rose from 4.6% in 2017
to 4.9% in 2018. The lending rates showed a decreasing trend from 4.9% in 2017 and 2018 to
3.4% in year 2021.
In my research, I had chosen 3 manufacturing firms in Malaysia that include Kossan
This part
can move to
chapter 3
Rubber Industries Berhad, Mah Sing Group Berhad and Tan Chong Motor Holdings Berhad to
see how corporate borrowings of these companies influenced by its determinants. The
determinants of corporate borrowings will be listed below:
a) Tangibility
student may first explain what is "tangibility".
Tangibility is an important variable to determine corporate borrowing because it will influence
firms cost of borrowings when firms applied for borrowings. As firms fixed assets increases,
elaboration needed.
interest rate charge to firms become fewer (Shah & Hijazi, 2004). In that case, we can expect
that firms with larger fixed assets can attain more borrowings (Shah & Hijazi, 2004). On the
other hand, firm with lesser fixed assets may only able to apply for fewer borrowings (Shah &
Hijazi, 2004). The reason is firm with fewer fixed assets will be incurred high interest rates
when they apply for borrowings (Shah & Hijazi, 2004).
2
CHAPTER 1: RESEARCH INTRODUCTION
b) Size
Large firms can enter capital market easily (Mustapha et al, 2011). Therefore, large firms will
borrow if they can accept the interest rate on borrowings (Mustapha et al, 2011). Chow (2019)
stated that the reason why large firms easier to attain borrowings because they have lesser
may explain further on adverse selection issue
adverse selection issue. Thirdly, large firms had operating in the industry for many years ago,
so, their characteristics and attributes may know by other firms in the industry (Chow, 2019).
In that case, they are easier to get borrowings. In contrast, small business is more difficult to
get borrowings because they encounter serious adverse selection problems (Chow, 2019).
Secondly, small firms may not know by other industries very well and this made them facing
difficulties to get borrowings (Chow, 2019).
c) Growth
Ibrahim and Lau (2019) stated that growing companies is tougher to receive corporate
borrowings. Growing companies always in risky investments or projects (Ibrahim & Lau,
2019). Thus, growing companies might be incurred higher borrowing costs because of their
involvement in risky investments (Ibrahim & Lau, 2019). Thus, growing companies might bear
higher liability because being incurred higher borrowings costs. Due to higher debt level,
growing companies might face difficulties to apply corporate borrowings in future (Ibrahim &
Lau, 2019). The reason is companies’ creditworthiness may reduce because they had a lot of
unpaid debts.
d) Profitability
Seng and Thaker (2018) also stated that companies that achieve better growth rate, they tend
to have larger profitability (Seng & Thaker, 2018). A larger profitability indicated that firms
have lesser financial burden (Seng & Thaker, 2018). Hence, firms with higher profitability are
easier to apply corporate borrowings because they capable to pay borrowings inclusive interest
rate payment charged on corporate borrowings (Seng & Thaker, 2018). In contrast, firms with
lower profitability are not suitable to apply corporate borrowings (Seng & Thaker, 2018). The
reason is firms with lower profits may fail to cover its business cost of production if they apply
for corporate borrowings (Seng & Thaker, 2018).
3
CHAPTER 1: RESEARCH INTRODUCTION
e) Leverage
Leverage is being determined by tangibility, size, growth and profitability (Shah & Hijazi,
Does it mean leverage is one of the independent variables? If that's the case, leverage shouldn't be your dependent variable in Chapter 3.
2004). A higher leverage indicates firm suffer larger debt burden (Shah & Hijazi, 2004). In that
case, firms discourage to make corporate borrowings (Shah & Hijazi, 2004). The reason is firm
may suffer much higher debt level if they resume make corporate borrowings (Shah & Hijazi,
2004). On the other hand, firm with a lower leverage indicates firm suffered low debt
repayment (Shah & Hijazi, 2004). In this case, firms can make decision whether want to apply
for corporate borrowings (Shah & Hijazi, 2004).
4
CHAPTER 1: RESEARCH INTRODUCTION
1.2 Research Background
First of all, research showed that the relationship between assets tangibility and leverage
is directly related (Shah & Hijazi, 2004). The reason was companies that owned bigger quantity
of fixed assets can made borrowings at a very low interest rates by providing assets securities
to the creditors (Shah & Hijazi, 2004). In contrast, firms that only have little amount of fixed
assets still can made borrowings but they need to pay with a higher interest rate (Shah & Hijazi,
2004). According to Rajan and Zingales (1995), there was a positive correlation between
tangibility and leverage. The reasons were firms that owned more collateral is easier to covered
their liabilities. According to findings from Serrasquero and Rogao (2009) and Cespedes et al.
(2010), the relationship between tangibility and leverage is positive because as companies
gained more tangible assets such as furniture and cars, it was easier for them to eradicate debt
burden. According to theoretical judgement, trade off- theory stated that there should be a direct
link between tangibility and leverage because when companies got more and more tangible
assets, companies can use them to fund their liabilities. However, there was a contradict
findings from researchers on the positive relationship between assets tangibility and leverage.
According to Ahmad Sheik and Wang (2011), the relationship between assets tangibility and
leverage is inversely related. For evidence, according to Abor (2008) findings, he argued that
small and medium enterprises (SMEs) that gained higher level of fixed assets can utilise their
large amount of fixed assets to decrease their liabilities.
Apart from that, there is contradiction perception among researchers regarding the
relationship between size and leverage. Some researchers argue that there should be positive
relationship between size and leverage. According to Titman and Wessels (1988), there should
be a direct relationship between size and leverage because bigger companies does not consider
insolvency costs to influence leverage. Second, bigger firms are more diversified compared to
small firms, so, they may experience fewer insolvency possibilities. According to Morri and
Cristanziani (2009), as firms size grow, leverage will increase because larger companies are
more diversified because got stronger liquidity. According to Harris and Raviv (1991) findings,
size and leverage is positively related when they tested them by using cross sectional data.
According to Serrasquero and Rogao (2009), the relationship between company size and
leverage must be positive. According to Cespedes et al. (2010), the larger the size of companies,
the higher the leverage.
5
CHAPTER 1: RESEARCH INTRODUCTION
However, some researchers argue that there is a negative relationship between size and
leverage. According to Rajan and Zingales (1995), the relationship between size and leverage
is negative. The reason is big firms does not have much asymmetric information. Thus, large
firms may not undervalue new equity issue by them (Shah & Hijazi, 2004). Large firms tend
to finance their equity to prevent themselves suffered from debt (Shah & Hijazi, 2004).
According to a study on G-7 countries conducted by Rajan and Zingales (1995), they found
out one of company size from G-7 country has a negative relationship towards leverage.
Moreover, there are a lot perception regarding the relationship between growth and
leverage. According to Drobetz and Fix (2003), there is a direct relationship between growth
and leverage because growing firms have larger leverage. According to Cespedes et al. (2010),
there is a direct relationship between growth and leverage. According to Sinha (1992), the
relationship between growth and leverage is positive. The reason is as company growth
increases, companies tend to demand more funding (Ibrahim & Lau, 2019). Increasing demand
of fundings will rise their debt level. As companies’ debt level rises, leverage also rises
(Ibrahim & Lau, 2019). Some researchers argue that growth and leverage is negatively related.
According to researchers such as Titman and Wessels (1988), Barclay et al. (1995) and Rajan
and Zingales (1995), the relationship between growth and leverage is inversely related.
According to Chadha and Sharma (2015), the relationship between growth and leverage is
negative. However, some researchers argue that there is no relationship between growth and
leverage. According to Karadeniz et al. (2009) and Ahmad Sheikh and Wang (2011),
companies’ growth does not relate to the debt level of lodging companies in Turkey and
manufacturing firms in Pakistan. So, when growth cannot influence debt, at the same time, it
could not influence on companies leverage. This inferred that the relationship between growth
and leverage does not exist. According to a study done by Serrasquero and Rogao (2009), he
found out that growth in Portugese companies is unable to determine its company leverage.
Finally, findings regarding relationship between profitability and leverage shown
contradiction. Trade off theory argue that the relationship between profitability and leverage
should be positive (Chow, 2019). The reason is profitable companies got larger leverage to
alleviate agency costs (Chow, 2019). Some researchers argue that the relationship between
profitability and leverage should be positive. According to Jensen (1986), profitability can
influence leverage directly if corporate borrowings in the market are effective. On the other
side, the relationship between profitability and leverage can be inversely related according to
6
CHAPTER 1: RESEARCH INTRODUCTION
theory and arguments by several researchers. According to pecking order theory, the
relationship between profitability and leverage is negatively related (Chow, 2019). The reason
is profitability firms does not rely on leverage (Chow, 2019). The problems of asymmetric
information and adverse selection encourage firms utilised retained earnings to make
themselves safer (Chow, 2019). According to Myers and Majluf (1984), they inferred that there
is a negative relationship between profitability and leverage because firms with profits will
have lesser leverage. The reason is firms with higher profitability can use finance in their
companies to finance their business and investment activities (Shah & Hijazi, 2004). Whereas,
firms that earn lesser profits have lesser ability to finance their business (Shah & Hijazi, 2004).
Thus, they may need to seek borrowings to finance their investments and projects (Shah &
Hijazi, 2004). By this way, small firm may suffer high debt level. According to Ohman and
Yazdanfar (2017), profitability is inversely related to leverage (Ibrahim & Lau, 2019). The
reason is as profitability increases, firms will not use external funds to finance his business and
this will reduce total debt bear by firm (Ibrahim & Lau, 2019). As total debt drops, firm
leverage also falls (Ibrahim & Lau, 2019). Research done by researchers such as Abor (2008),
Serrasquero and Rogao (2009), Cespedes et al. (2010), Ahmad Sheikh and Wang (2011), Frank
and Goyal (2009), Cekrezi(2013) and Alipour et al. (2015) also agree that profitability and
leverage are inversely related.
student merely explain the relationship between variables based on past studies/theory, without identify the research gap.
7
CHAPTER 1: RESEARCH INTRODUCTION
1.3 Problem Statement
The main reason that attracts me to study corporate borrowings is due to increasing
but your paper wasn't study the impacts of it?
of corporate borrowings. Second, I intend to observe how corporate borrowings can influence
firms negatively.
Corporate sector debts in Malaysia
2016
2017
2018
2019
2020
2021
109.8%
108%
108%
102.6%
101.3%
99.1%
2016
Figure 2: XXX
Source: XXX (Italic)
check for the rest.
2017
2018
2019
2020
2021
Figure 2: Corporate sector debts in Malaysia from year 2016 to 2021 (The Edge 2023)
Figure 2 indicates trend of total debts bear by corporate companies listed in Malaysia from
year 2016 to year 2021. Initially, total debts bear by corporate companies in Malaysia drop
from 108% in 2016 to 101.3% in 2017. A drop of total debt from year 2016 to year 2017
indicates that corporate firms in Malaysia make lesser borrowings. There is a slight increase of
1.3% of total debt in 2018 compared to year 2017. A slight increase of corporate companies’
debt level is caused by a small increase of corporate borrowings made by corporate companies.
Later, corporate borrowings fall from 102.6% in 2018 to 99.1% in 2019. This small reduction
of total debts is because business demand lesser corporate borrowings. However, there is a
rapid increase of 10.7% of total debt in year 2020 compared to year 2019. This huge increase
of total debts is because most companies apply borrowings to finance their business and
investment activities. Total debt bear by companies dropped slightly from 109.8% in year 2020
to 108% in 2021. This indicates that total debt bear by corporate firms in Malaysia during year
2021 has not much difference compared to year 2020.
I understand that increase (decrease) in corporate companies' total debt means corporate companies borrow more (less), but why this happen
during that time?
8
CHAPTER 1: RESEARCH INTRODUCTION
Companies
Total Borrowings (RM billion)
Tenaga Nasional
53.42
YTL Corp
42.37
Genting
39.00
YTL Power International
27.84
Axiata Group
18.66
IOI Properties
16.50
MISC
15.52
SP Setia
12.34
Batu Kawan
12.15
Genting Malaysia
11.65
Kuala Lumpur Kepong
11.02
Sapura Energy
10.69
Maxis
9.93
Malakoff Corp
9.74
Yinson Holdings
9.30
IHH Healthcare
8.62
DRB-Hicom
7.93
Sunway
7.78
Aeon Credit Service M
7.50
Boustead Holdings
6.80
Hap Seng Consolidated
6.74
Press Metal Aluminium
6.70
Sime Darby Plantation
6.54
Bumi Armada
6.50
Ekovest
6.41
Telekom Malaysia
5.71
Malaysia Airports
5.32
Berjaya Corp
5.09
IOI Corp
5.08
Gamuda
4.98
9
Digi.com
4.75
UEM Sunrise
4.21
FGV Holdings
4.19
IGB
4.12
Tropicana Corp
3.88
WCE Holdings
3.83
Malayan Cement
3.80
Sime Darby Property
3.79
Petronas Gas
3.56
Serba Dinamik
3.55
Sunway REIT
3.40
Berjaya Land
3.37
Leong Hup International
3.04
Ahmad Zaki Resources
2.97
WCT Holdings
2.96
UM Corp
2.95
OSK Holdings
2.68
Astro M Holdings
2.68
UMW Holdings
2.65
Eco World Development
2.58
Figure 3: Top 50 companies in Malaysia with larger corporate borrowings. (The Edge 2023)
According to Figure 3, Tenaga Nasional is the company achieved the top corporate
borrowings with RM53.42 billion. This implies that Tenaga Nasional bear the highest debt
compared to other companies listed from Figure 3. However, YTL Corp and Genting are the
second and third companies that have larger corporate borrowings amounted to RM42.37
billion and RM39 billion respectively. This indicates that both YTL Corp and Genting have
higher debt level since their borrowings is higher but both companies’ debt level is lesser
compared to Tenaga Nasional. Eco World Development is the company demand the least
corporate borrowings among top 50 corporate borrowings companies with an amount of
RM2.58 billion. This showed that Eco World Development suffered the least debt level
compared to other top 50 corporate borrowings companies.
A lot of redundant sentences. Please check and remove.
10
isn't it the same?
Big companies are facing the problem of defaulting the loan. Is the size of company (or any other factors) still matter on the amount of borrowing that company
get? Will large company still able to get large amount of loan in future? And this could be why you want to study the determinants of corporate borrowing?
Try to link the issues to your topic.
CHAPTER 1: RESEARCH INTRODUCTION
According to Malaymail (2023), Evergrande, big property company in China force to
pay back borrowings that owed. The China authorities barred Evergrande continue demand
borrowings because borrowings made by this company was too much (Malaymail 2023). On
the other hand, small companies did not pay for loans and therefore government had tightened
lending policy (Malaymail 2023). This measure taken by China authorities cause small firms
unable to generate liquidity because they unable to borrow (Malaymail 2023).
According to The Edge Market, Sapura Energy Berhad faced higher debt level due to
its company higher demand on corporate borrowings. Sapura Energy Berhad wrote a proposed
scheme to the authorities in Malaysia (The Edge 2023). The aim is to attain permit from
Malaysia government to give them extension for debt payments (The Edge 2023). According
to the scheme presented by Sapura Energy Berhad, they requesting the government give them
4 years to settle their debt payments and only 5% of total owing will be paid during year 1(The
Edge 2023). According to The Star Newspaper (2023), Sapura Energy Berhad inclusive its 22
subsidiaries successfully attain permit from government regarding their debt payment
extension.
Corporate borrowings from non-financial sector in
advanced economis and emerging market economics
Advanced Economies
Emerging market economies
Rates(%)
242.9
125.8 111.6
242.5 238.8
177
146.5 144.1 135.6 149.7 157.3 162.3
187.7
200.3
188.9
226
203.1
320.8
267
268.8 261.2 270.9 267.8 255.7 266.6 265
276.9 264.7 274.1
288
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Year
Figure 4: Corporate borrowings made by non-financial sector in advanced and emerging
market economics during year 2007 to year 2021. (Bank for International Settlements n.d)
11
CHAPTER 1: RESEARCH INTRODUCTION
Countries involved in advanced economics consist of Canada, France, Germany, Italy,
Japan, United Kingdom and G7 countries. Whereas, countries involved in emerging market
economics are India, Mexico, Russia, Pakistan, Saudi Arabia, China and Brazil. According to
Figure 4, both advanced economies and emerging market economies achieved the top corporate
borrowings in year 2020 with 320.8% and 242.9% respectively. This inferred that there is a
massive number of firms from non-financial sector demand corporate borrowings during year
2020. Both advanced economics and emerging market economies achieved the least corporate
borrowings in year 2008 with 238.8% and 111.6% respectively. However, the moderate growth
of corporate borrowings for advanced economies happened during 2014 and 2015 with 267.8%
and 255.7% respectively. On the other hand, moderate growth of corporate borrowings from
emerging market economies also occurred in year 2014 and 2015 with 157.3% and 162.3%
respectively. The moderate growth of corporate borrowings occurred on emerging market
economies and advanced economies during 2014 and 2015 because most companies use
internal funds to finance their investment and business. When most business rely on internal
funds, borrowings activities reduce and eventually lead to a fall in corporate borrowings. In
year 2021, the corporate borrowing from advanced economies is 288% which is higher than
emerging market economies with 226%. This implies that total debt bear by advanced
economies is higher than emerging market economies.
Non financial corporation corporate
borrowing
Rates(%)
121
75.8
74.4
71.9
92
88.8
84.7
58.7
86.1
80.8
87
90.6
87.3
86.9
82
100.9
102.4
86.9
86.5
106.9
92.2
97.9
88.4
110.4
103.3
91.4
104.4
94.2
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Year
Advanced economies
Emerging Market economies
Figure 5: Corporate borrowings made by non-financial corporation in advanced and emerging
market economics during year 2008 to year 2021. (Bank for International Settlements n.d)
12
CHAPTER 1: RESEARCH INTRODUCTION
According to Figure 5, both non-financial corporations in advanced economies and
emerging market economies countries have the top corporate borrowings in year 2020 with
104.4% and 121% respectively. This indicates that non -financial corporations in advanced
economies and emerging market economies bear the highest debt in 2020. The least corporate
borrowings bear by non-financial corporations in advanced economies is 82% during 2014. On
the other hand, the lowest corporate borrowings demand by non-financial corporations in
emerging market economies was 58.7% during 2021. The lowest corporate borrowings implies
that non- financial corporations in emerging market economies and advanced economies bear
the lowest debt level. In year 2021, corporate borrowings made from non-financial corporations
in emerging market economies was 110.4% which is larger than advanced economies with
94.2%.
13
CHAPTER 1: RESEARCH INTRODUCTION
1.4 Research Questions
The purpose of this study is to find out answers for questions listed below:
i)
What is the relationship between assets tangibility and leverage in Malaysia?
ii)
What is the relationship between size and leverage in Malaysia?
iii)
What is the relationship between growth and leverage in Malaysia?
iv)
What is the relationship between profitability and leverage in Malaysia?
1.5 Research Objectives
1.5.1 General Objectives
This study done with a purpose to investigate determinants of corporate borrowings in
Malaysia.
1.5.2 Specific Objectives
i)To investigate the relationship between assets tangibility and leverage in Malaysia.
ii)To examine the relationship between size and leverage in Malaysia.
iii) To clarify the relationship between growth and leverage in Malaysia.
iv) To determine the relationship between profitability and leverage in Malaysia.
14
CHAPTER 1: RESEARCH INTRODUCTION
1.6 Significance of study
This study is important because it can influence companies that involved in corporate
borrowings in Malaysia. The most important role in this research is to ensure able to identify
how realizing the factors to reduce the negative impacts?
all determinants of corporate borrowings. Once the determinants able to identify, the
government can take measures to reduce the bad impact of corporate borrowings. According
to past studies done by former researchers, the researchers has illustrated the relationship
between tangibility, size, growth and profitability and leverage. However, there is contradiction
viewpoints about the relationship between tangibility, size, growth, profitability, and leverage.
Therefore, to solve the arguments among researchers regarding the relationship between
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tangibility, size, growth and profitability, I will conduct my own study to analyse how these 4
independent variables that include tangibility, size, growth and profitability able to influence
leverage.
Moreover, this study is also important because it help firms able to identify and
understand all determinants that determine corporate borrowings especially when they demand
for corporate borrowings. When firms can identify determinants of corporate borrowings, firms
can make a wiser decision when they demand for corporate borrowings. For example, firms
can decide on how much they intend to borrow and at the same time, they can ensure money
that borrow can successfully repay without hurting their balance sheet much. If firms can repay
the borrowings without selling their assets, therefore, their company total assets in balance
sheet will not reduce a lot. Even though there are some former researchers had conducted
studies to analyse the determinants of corporate borrowings in manufacturing industry but data
that taken in their study does not include current data. Past data selected by researchers to
analyse determinants of corporate borrowings is fail to help us analyse current trend on
determinants of corporate borrowings. Therefore, I will conduct my personal research to find
out the past and present data related to determinants of corporate borrowings.
15
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CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
In this chapter, it is separated into 2 parts which is theoretical framework and review of
past literature. Under theoretical framework part, it explained about theories that related in this
research. Theories that related in this research are trade off theory, pecking order theory and
agency cost theory. The independent variables that used in this study are tangibility, size,
growth and profitability. Dependent variable used in this study is leverage. The second part of
this chapter, review of past literature is about the summary made by former researchers to
explain the relationship between independent variables and dependent variables. The purpose
of doing past literature review is to help readers understand how the independent variables can
influence leverage in Malaysia.
2.2 Theoretical Framework
Student does not have to explain the variables with theory, yet student should have to explain the theory
with variables. For example, 2.2.1 Trade-off Theory - explain how this trade off theory link the
relationship between the variables, then 2.2.2 Pecking Order Theory - explain .... and etc.
According to trade off theory, there is a direct relationship between tangibility and
leverage (Chow, 2019). A larger tangibility of assets indicates that companies able to acquire
more fixed assets based on loans attained by them (Chow, 2019). For example, companies that
successfully applied large amount of loaning from financial institution can use for purchasing
more fixed assets such as furniture, buildings and machinery or factory that need to use by
them in production. On other side, firms that owned tremendous fixed assets are easier to apply
borrowings (Chow, 2019). The reason is firms can use certain amount of fixed assets own by
them as a collateral when apply for borrowings (Rus & Samiran, 2012). When firms used
collateral for making borrowings, firms will bear lesser interest rate payments and firms does
not need to use any equity to finance their debt because got collateral (Rus & Samiran, 2012).
In this case, the borrower will not suffer losses because they can confiscate firms’ collateral if
companies fail to repay borrowings (Rus & Samiran, 2012).
However, the relationship between assets tangibility and leverage are different according
to pecking order theory. According to pecking order theory, firms with fewer tangibility assets
may face problems such as information asymmetry and adverse selection (Chow, 2019). Firms
that owned lesser tangible assets still possible to make borrowings but they may face huge debt
level if fail to repay loans (Chow, 2019). Under pecking order theory, companies that own
many fixed assets faced fewer asymmetric information problems. However, according to
Yusuf, Yunus and Supaat (2013), some firms also finance their debt with equity but the
relationship between assets tangibility and leverage appeared indirect (Yusuf et al, 2013).
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CHAPTER 2: LITERATURE REVIEW
Next, the relationship between size and leverage can be explained through trade off
theory and pecking order theory. According to trade off theory, firms with bigger size have less
possibility to encounter bankruptcy problems (Yusuf et al, 2013). The reason is larger firms
can negotiate for better credits or borrowings deals in financial market due to their good
business records and financial condition (Yusuf et al, 2013). The second reason is larger firms
would tend to diversify their business (Vatavu, 2013). As firms diversify their business,
bankruptcy problems can reduce because firms have more financial capability to repay their
debts as they diversify their business (Vatavu, 2013). For instance, Berjaya Corporation Berhad
is a type of firm that diversify its business into various types such as marketing and retail,
property, hotels and resorts, casino services and education. However, smaller firms have higher
possibility to encounter bankruptcy problem (Rus & Samiran, 2012). The reason is small firms
encounter information asymmetry in the market (Rus & Samiran, 2012). Small firms may need
to pay higher costs to gain all information in the market (Rus & Samiran, 2012). Therefore,
small firms are discouraging to seek for external finance such as borrowings and loans offered
by financial institution. Small firms may fail to repay borrowings and interest rate charge by
the party since they had spent huge financing to attain information in the market (Rus &
Samiran, 2012). If small firms default their borrowings payments, their debt level will increase
(Rus & Samiran, 2012). Second, small firms cannot diversify their business due to their small
size and petty funds, thus, this may increase their bankruptcy and leverage if they choose to
perform it (Yusuf et al, 2013). Thus, trade off theory indicated that the relationship between
size and leverage is positive because bigger firms have larger leverage while smaller firms have
smaller leverage (Chow, 2019).
According to pecking order theory, the larger the size of a firm, the lower the leverage
of a firm. So, this theory proved that there is a negative relationship between size and leverage
(Chow, 2019). According Pecking order theory, big firms still encounter information
asymmetry issue but their information asymmetry less serious compared to small firms (Yusuf
et al, 2013). The reason why bigger firms faced less serious information asymmetry because
they have more funds to purchase the information available in market. Apart from having larger
funds to purchase information, bigger firms stay longer in the market compared to small firms.
Thus, bigger firms have well versed knowledge about market compared to small firms. Next,
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CHAPTER 2: LITERATURE REVIEW
bigger firms are more known by market participants in the industry because they existed in the
industry many years ago. So, bigger firms might build good rapport with various market
participants, thus, they may easier to receive any new updates information in the market. Since
large firms have less information asymmetry, therefore, large firms may able to attain
borrowings with lower interest rate payments. The lower the interest rate paid by firms, the
lesser the debt level and leverage bear by firms. Bigger firms are easier to issue equity to pay
their debts because they have lesser adverse selection problem (Chow, 2019). Sometimes, big
firms are not necessarily need to apply borrowings because they can utilise company profits or
equity to finance their investment projects. As firm finance their projects by using internal
funding, companies leverage will reduce because firms does not owe other parties’ loans and
interest rate payments since they are not borrowed (Vatavu, 2013).
The relationship between growth and leverage can be explain by certain theories such
as trade-off theory, agency theory and pecking order theory. According to Trade off theory,
intangible assets is a tool to measure company growth opportunities (Yusuf et al, 2013). In this
case, the intangible assets are referred to companies’ growth in future (Yusuf et al, 2013). If
companies have better future growth, companies may reduce borrowings in future because
companies can rely on internal finance to support their future business activities (Yusuf et al,
2013). Since companies have higher possibility shown better future growth, at the same time,
companies may not face financial issues in future. On the other hand, companies that hold
tangible assets such as inventory, machinery and equipment tend to borrow more compared to
companies that depend on intangible assets (Yusuf et al, 2013). The reason is tangible assets
hold by firms is unable to support future growth. When small firms borrow more, they can use
funding to boost business and investment activities which could improve their future growth.
According to trade off theory, when market-to-book-ratio is high enough, this indicates
companies have better investment growth in future (Chow, 2019). Thus, companies may reduce
borrowings now because companies need to spend a lot of money to finance their future
investments activities (Chow, 2019). As firms borrowed less, leverage will drop (Chow, 2019).
Thus, trade off theory said that there is a negative relationship between growth and leverage
(Chow, 2019).
According to pecking order theory, firms may need larger funds to finance their
investment activities (Chow, 2019). Firms may face insufficient funds to finance investment
activities if they only rely on internal funds (Chow, 2019). Thus, firms may decide to increase
borrowings to finance their investment activities in future (Chow, 2019). According to pecking
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CHAPTER 2: LITERATURE REVIEW
order theory, firms that under growing process are more likely faced insufficient funds to
finance their investment activities (Shah & Hijazi, 2004). Thus, growing firms may apply
borrowings from banks to finance investment activities but this could lead them to a higher
debt and leverage (Shah & Hijazi, 2004). When growing firms have higher debt record,
growing firms may face poor credibility which make them tougher to apply borrowings in
future (Shah & Hijazi, 2004). Moreover, pecking order theory also stated that there is
information asymmetry between managers and investors (Mustapha et al, 2011). Managers will
have more information regarding company share prices compared to investors (Mustapha et al,
2011). Investors will not be informed by the managers about the trend of share price (Mustapha
et al, 2011). Investors may face higher risk if they buy shares from companies that not perform
well (Mustapha et al, 2011). Companies share price will continue drop if they showed
continuous poor performance in the market (Mustapha et al, 2011). This ends up investors bear
higher debt and leverage because they suffer losses from investments (Mustapha et al, 2011).
Therefore, pecking order theory mentioned that growth and leverage supposed to be directly
related because as growth increases, leverage will increase (Mustapha et al, 2011).
According to agency cost theory, growing firms tend to suffer higher agency costs
compared to non-growing firms. The reason is because growing firms shown greater
involvements in risky projects or investments activities (Shah & Hijazi, 2004). Since lenders
realised that growing firms possessed greater opportunity earn higher profits in future,
therefore, they may impose higher borrowings costs to growing firms (Shah & Hijazi, 2004).
This caused growing firms suffered higher debt level as well as increasing leverage (Shah &
Hijazi, 2004). According to agency cost theory, firms that based on equity invest slowly
because they intend to take away wealth earned from entrepreneurs in the industry (Ibrahim &
Lau, 2019). As growing entrepreneurs’ wealth taken by equity firms, this may cause growing
entrepreneurs suffered higher debt level because they do not have enough funds to pay back
their debts especially borrowings and loans which have not pay completely (Ibrahim & Lau,
2019). In that case, to prevent growing entrepreneurs confronting increasing debt level in
future, it is advisable for them not to apply borrowings very much (Ibrahim & Lau, 2019).
Thus, agent cost theory stated that the relationship between growth and leverage should be
negative (Yusuf et al, 2013).
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CHAPTER 2: LITERATURE REVIEW
Finally, the relationship between profitability and leverage can be explained by certain
theories such as trade off theory and pecking order theory. According to trade off theory,
profitable firms faced fewer financial problems (Chow, 2019). Even though profitable firms
faced lesser financial problems but they also faced a higher leverage because agency costs in
the market cause taxable income rises (Chow, 2019). Firms may choose to make borrowings
for paying their taxable incomes (Yusuf et al, 2013). By using borrowings for taxable income
payments, it could reduce interest rate payment on taxable income (Yusuf et al, 2013).
According to trade off theory, profitable companies are more likely to apply borrowings to
settle their taxable income payments because they have a lot of fixed assets which can serve as
collateral of borrowings (Vatavu, 2013). Thus, trade off theory stated that the relationship
between profitability and leverage is directly related (Chow, 2019).
According to pecking order theory, profitability is a variable that indicate how strong
is a firm cash flow and how less total debt bear by companies (Ibrahim & Lau, 2019). External
debts such as loans and borrowings will reduce companies’ profits because companies need to
take certain parts of their profits to settle their debts (Ibrahim & Lau, 2019). According to
pecking order theory, high profits companies will finance their investment projects by using
their own company financing such as retained earnings (Rus & Samiran, 2012). High profits
firms will have higher retained earnings because they successfully gained a lot of sales. Since
high profits companies have higher retained earnings, therefore, they have surplus funds to
finance their investment projects (Rus & Samiran, 2012). Therefore, high profits firms do not
need to apply borrowings or loans from outside parties such as financial institutions (Rus &
Samiran, 2012). Since high profits companies do not opt for borrowings, they will not bear any
external debts (Rus & Samiran, 2012). Therefore, high profits companies will bear lower total
debt and leverage (Rus & Samiran, 2012). Pecking order theory also mentioned that firms will
only apply loans and borrowings if there are insufficient funds in their company to finance the
investment projects (Shah & Hijazi, 2004). For instance, if firms earn lower retained earnings
and profits, firms may seek for external financing such as apply bank loans to cover their cost
of investments projects (Shah & Hijazi, 2004). Therefore, in that case, if firms apply
borrowings due to insufficient internal funding, this will lead to a rise of company debt level
and leverage (Shah & Hijazi, 2004). So, the negative relationship between profitability and
leverage stated by pecking order theory is precise because firms with lower retained earnings
will suffer high leverage (Ibrahim & Lau, 2019).
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CHAPTER 2: LITERATURE REVIEW
2.3 Review on the previous literatures
2.3.1 Tangibility and leverage
Tangibility is referred to firms fixed assets (Rus & Samiran, 2012). Fixed assets are liquid
because it can easily convert into cash (Rus & Samiran, 2012). For example, if firms fail to
apply loans or borrowings, firms can sell away fixed assets to generate cash. By this way, firms
can avoid debt burden and higher leverage because firms do not suffer any debt on loan
repayments since they did not apply for borrowings. If firms only own little fixed assets, firms
unable to sell away those fixed assets to generate liquidity. The reason is firms’ production will
be badly affected if they sell away those useful assets that need in production process. For
example, a tire manufacturing company sell away machinery specializing in producing tires
because it wants to generate liquidity. Thus, it does not need to apply borrowings because can
generate liquidity. Therefore, it will have a zero leverage because does not bear any debt.
However, there is a limitation on this tire manufacturing company method where it may suffer
low tires production. Tires production is lower because there is shortage of machinery that
specializing in producing tires. So, firms with fewer fixed assets that need to finance their
company projects are more advisable to apply borrowings. Even though they may face larger
debt and leverage but they can reduce their debt through repaying their borrowings.
A lot of previous researchers has illustrated about the relationship between tangibility and
leverage. Some researchers argue that the relationship between tangibility and leverage should
be positive. Yusuf et al (2013) conducted a study on how tangibility can influence leverage in
do not abbreviate when it is first mentioned.
Malaysia from 1997 to 2011. They took a total of 11 E&E manufacturing companies that listed
in Bursa Malaysia. Yusuf et al (2013) adopted pooled Ordinary Least Square (OLS) to test the
relationship between asset tangibility and leverage. The results revealed that tangibility had
direct relationship towards leverage (Yusuf et al, 2013). For evidence, the coefficient of
tangibility is a positive value which is 0.29819 and this showed that each 1% increase in
tangibility will lead to leverage, on average, increase by 0.29819% vice versa and ceteris
paribus (Yusuf et al, 2013). According to Yusuf et al (2013), the positive relationship between
tangibility and leverage indicated that the more the tangible assets hold by firms, the more the
debts they able to settle. There are some reasons that lead to a positive relationship between
tangibility and leverage. Firstly, tangible assets can serve as a collateral when apply for
borrowings (Yusuf et al, 2013). For example, companies can used cars and land to serve as a
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CHAPTER 2: LITERATURE REVIEW
collateral from borrowings. Secondly, tangible assets can give security for creditors (Yusuf et
al, 2013). Creditors can take away tangible assets that used by firms as collateral if firms’
default their payments (Yusuf et al, 2013). For example, the bank can confiscate firms’
collateral assets when firms unable to repay borrowings. In that case, creditors will not suffer
losses even though firms default their payments (Yusuf et al, 2013).
According to Ibrahim and Lau (2019), they studied about the relationship between
tangibility and leverage in Malaysia listed companies under food and beverage sector from
2000 to 2015. A total of 151 Malaysia listed companies had taken to study the relationship
between tangibility and leverage (Ibrahim & Lau, 2019). By adopting multiple regression
model to find out the relationship between tangibility and leverage, they found out that
tangibility and leverage are directly related (Ibrahim & Lau, 2019). For evidence, the
coefficient of tangibility from multiple regression model is 0.016 which is a positive value and
this indicates that each 1% increase in tangibility will cause leverage, on average increase by
16% (Ibrahim & Lau, 2019). Long term debt is a factor that cause the positive relationship
between tangibility and leverage (Ibrahim & Lau, 2019). For example, firms are given longer
repayment periods for borrowings made by them. Therefore, firms have plenty of time to decide
on how much tangible assets that able to convert into liquidity to repay their borrowings which
will due in long term.
According to M’ng, Rahman and Sannacy(2018), they studied the relationship between
tangibility and leverage in Malaysia from period 2004 to 2013. M’ng et al (2018) also took a
sample of 911 companies that listed in Bursa Malaysia to perform their study on the
relationship between tangibility and leverage. M’ng et al (2018) used pooled Ordinary Least
Square to test the relationship between tangibility and leverage. Result found out that there is
a positive relationship between tangibility and leverage (M'ng et al, 2018). For evidence, the
coefficient for tangibility is 0.1231 which is a positive value (M'ng et al, 2018). It means that
each 1% increase in tangibility can lead to leverage, on average increase by 0.1231 % (M'ng et
al, 2018). There is a reason that caused the positive relationship between tangibility and
leverage. According to M’ng et al (2018) viewpoint, sometimes, firms may not have enough
assets to sell in order to support their investments projects. Therefore, firms may choose to
demand borrowings to cover their cost of investments (M'ng et al, 2018). Increasing borrowings
made by firms may lead firms suffered higher debt and leverage (M'ng et al, 2018).
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CHAPTER 2: LITERATURE REVIEW
According to Jaafar, Muhamat, Ahmad and Alwi (2017), they investigated the relationship
between tangibility and leverage by adopting 34 Malaysia firms based on shariah principle and
all these 34 Malaysia companies are registered in Bursa Malaysia. The study period ranging
from 2006 to 2016 (Jaafar et al, 2017). In order to study the relationship between tangibility
and leverage, multiple regression need to form by regressing variables such as tangibility,
profitability, size and growth as independent while leverage regressed as dependent variable
(Jaafar et al, 2017). The result found out that the relationship between tangibility and leverage
is positive because the coefficient for tangibility is 0.029 which is a positive value (Jaafar et al,
2017). Thus, this inferred that each increase of RM1 will lead to the leverage, on average,
increase by RM0.029 vice versa and holding other constant (Jaafar et al, 2017). The positive
relationship between tangibility and leverage also supported by former researchers such as
Hadianto (2008), Mas’ud(2008), Kartika(2009), Murhadi(2011) and Munawar (2012). The
reason why most previous researchers argue that the relationship between tangibility and
leverage should be positive because they believed that firms should use their assets hold by
them to act as a collateral for borrowings (Jaafar et al, 2017). The more assets’ firms used as
collateral, the higher the borrowings made by firm (Jaafar et al, 2017). But there is one
limitation in this method where firms may suffer higher leverage because they need to repay
huge number of borrowings in future (Jaafar et al, 2017).
Nejad and Wasiuzzaman (2013) employed Pooled Ordinary Least Squares (OLS) to
test the relationship between tangibility and leverage in 177 companies listed in Bursa
Malaysia. Under Pooled OLS regression, tangibility regressed as an independent variable
whereas leverage regressed as a dependent variable (Nejad & Wasiuzzaman, 2013). Data on
tangibility and leverage from the 177 Malaysia companies took from period 2005 until
2010(Nejad & Wasiuzzaman, 2013). After that, Nejad and Wasiuzzaman (2013) run the results
of tangibility and leverage, they found out that the relationship between tangibility and leverage
is positive. (Nejad & Wasiuzzaman, 2013) For evidence, the coefficient for tangibility is
0.0187768 which is a positive value. This implied that each 1% increase in tangibility will
cause leverage, on average, increase by 0.0187768 % vice versa and ceteris paribus (Nejad &
Wasiuzzaman, 2013). Nejad and Wasiuzzaman (2013) stated that the positive relationship
between tangibility and leverage can be explained by trade off theory (Nejad & Wasiuzzaman,
2013). Trade off theory stated that increase in tangibility can reduce firms’ debt payments
because firms does not need to make borrowings (Nejad & Wasiuzzaman, 2013). However,
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CHAPTER 2: LITERATURE REVIEW
leverage will increase because firms increase the usage of tangible assets to finance their
investment projects (Nejad & Wasiuzzaman, 2013).
On the other hand, some researchers argue that the relationship between tangibility and
assets should be negative. According to Chow (2019), he carried out research to study the
relationship between tangibility and leverage in Malaysia consumers product sector between
2000 to 2014 by using Seemingly unrelated Regression (SUR) estimation. The findings found
out that the relationship between tangibility and leverage is negatively related. For evidence,
the coefficient for tangibility under consumer product in SUR estimation is -0.756 and this
indicated that each 1% increase in tangibility will cause leverage on average reduce by 0.756%
vice versa and ceteris paribus (Chow, 2019). According to Chow (2019), there are some reasons
why tangibility and leverage are negatively related. Firstly, companies that own lesser fixed
assets have a lot of information asymmetry issues (Chow, 2019). Firms with lesser fixed assets
may suffered larger debt level and leverage because they rely on borrowings to finance their
business activities (Chow, 2019). Second, firms with fewer fixed assets also faced adverse
selection problems because they cannot gain perfect information in the market (Chow, 2019).
For instance, they may not know the cheaper borrowings rate or interest rates available in the
whole market. Therefore, this ends up firms with fewer fixed assets force to make borrowings
no matter how high interest rates on borrowings incurred to them since they cannot used fixed
assets to generate liquidity to support their business activities. Thus, this caused firms with
fewer fixed assets will have a higher leverage because they suffered higher debt repayment
(Chow, 2019).
According to Rus and Samiran (2012), they conducted a study regarding the relationship
between tangibility and leverage in 50 constructions companies in Malaysia from period 2003
to 2008. Rus and Samiran (2012) adopted basic Ordinary Least Squares (OLS) to test the
relationship between tangibility and leverage. The result revealed that the coefficient for
tangibility is -0.170 which is a negative value. (Rus & Samiran, 2012) This inferred that each
1% increase in tangibility will lead to leverage, on average, dropped by 0.170% vice versa and
ceteris paribus (Rus & Samiran, 2012). From this outcome, researchers such as Rus and
Samiran (2012) concluded that there is a negative relationship between tangibility and leverage.
According to Rus and Samiran (2012), construction companies in Malaysia does not have a lot
of assets such as land and properties. Although the construction companies owned many
constructions equipment but the construction equipment is rejected by banks to serve as
collateral (Rus & Samiran, 2012). Therefore, the construction companies in Malaysia will
24
CHAPTER 2: LITERATURE REVIEW
finance their investment projects by using internal fundings (Rus & Samiran, 2012). As the
construction firms using internal fundings to fund their investment projects, this will reduce
their debt burden and leverage (Rus & Samiran, 2012). Thus, this established a negative
relationship between tangibility and leverage and this negative relationship is consistent with
Pecking Order Theory (Rus & Samiran, 2012).
According to Saarani and Shahadan (2013), they conducted research on the
relationship between tangibility and leverage. In the process of doing this research, Saarani and
Shahadan (2013) selected 334 companies in Malaysia and took data on tangibility and leverage
from period 2005 to 2009. To test the relationship between tangibility and leverage, Saarani
and Shahadan (2013) employed Ordinary Least Square (OLS) by regressing tangibility as an
independent variable whereas leverage regressed as a dependent variable. The result revealed
that tangibility can influence leverage in an indirect way. For evidence, the coefficient for
tangibility is -0.217 which is a negative value (Saarani & Shahadan, 2013). This implied that
each 1% increase in tangibility will lead to leverage, on average declined by 0.217% vice versa
and ceteris paribus (Saarani & Shahadan, 2013). According to Saarani and Shahadan (2013),
short term debt payment is a factor that led to negative relationship between tangibility and
leverage. Firms faced time constraint to decide on how much assets they need to sell for
generating cash flow to settle short term borrowings payments (Saarani & Shahadan, 2013). If
the borrowings owed by firms is in a huge amount, they may decide to sell as much assets as
possible to cover their short-term borrowings payments without much consideration. Since
firms sell many assets to generate cash flow to settle short term debt, therefore, firms’
tangibility is higher and leverage is reducing due to debt reduction (Saarani & Shahadan, 2013).
According to Marimuthu and Hamzah (2020), they selected a sample of 28 oil and gas
companies in Malaysia to study the relationship between tangibility and leverage from period
2008 to 2017. Marimuthu and Hamzah (2020) applied balanced panel Unit Root test to test the
relationship between tangibility and leverage. The results found out that there is an indirect
relationship between tangibility and leverage (Marimuthu & Hamzah, 2020). For evidence, the
coefficient of tangibility is -8.1810 which is a negative value (Marimuthu & Hamzah, 2020).
This inferred that each 1% increase in tangibility will cause the leverage, on average reduce by
8.1810% (Marimuthu & Hamzah, 2020). According to Marimuthu and Hamzah (2020)
opinion, the negative relationship between tangibility and leverage is influenced by pecking
order theory. According to pecking order theory, the more tangible assets used by firms as
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CHAPTER 2: LITERATURE REVIEW
collateral, the more the borrowings successfully applied by firms (Marimuthu & Hamzah,
2020). Due to large amount of assets own by firms, they succeed applied more borrowings
with much a lower interest rate (Marimuthu & Hamzah, 2020). Hence, firms may bear fewer
debt burden and leverage since interest rate payments on borrowings is lower (Marimuthu &
Hamzah, 2020).
According to Azhari, Mahmud and Shaharuddin (2022), they studied on how tangibility
can determine leverage in Malaysia companies before and after Covid-19. To study the
relationship between tangibility and leverage, 348 public listed companies in Malaysia had
selected and data on tangibility and leverage from 2019 to 2021 had collected (Azhari et al,
2022). Azhari et al (2022) used panel Ordinary Least Squares (OLS) to study the relationship
between tangibility and leverage. The results stated that the relationship between tangibility
and leverage is negative before and after Covid-19(Azhari et al, 2022). For evidence, before
Covid-19, the tangibility coefficient is -0.1523(Azhari et al, 2022). This indicates that each 1%
increase in tangibility will cause leverage, on average reduce by 0.1523% vice versa and ceteris
paribus (Azhari at al, 2022). During Covid-19, the coefficient for tangibility is -0.1461(Azhari
at al, 2022). This showed that each 1% increase in tangibility will cause leverage, on average
reduce by 0.1461% vice versa and ceteris paribus (Azhari et al, 2022). Azhari, Mahmud and
Shaharuddin stated that the negative relationship between tangibility and leverage is influenced
by pecking order theory. According to pecking order theory, firms that own many fixed assets
can reduce their leverage more effectively. Firms can sell certain fixed assets own by them.
Through this way, firms can generate fund to finance their business projects without the need
for borrowings. Therefore, firms’ debt and leverage will decrease when they borrow less
(Azhari et al, 2022).
In conclusion, there is a contradiction viewpoint among researchers regarding the
relationship between tangibility and leverage. Some researchers argued that the relationship
between tangibility and leverage should be positive. Whereas, certain researchers argue that
the relationship between tangibility and leverage should be inversely related. This showed that
there is no direct answer about the relationship between tangibility and leverage.
2.3.2 Size and leverage
Size is an indicator that represent firms’ business operation (Chow, 2019). Generally,
large firms will have larger business operation (Chow, 2019). For example, since large firms
26
CHAPTER 2: LITERATURE REVIEW
involved in many business activities, therefore they may have a lot of budget records such as
journals and T-accounts that record monetary transactions. Large firms also may spend higher
training costs because they hired many workers. In marketing aspect, large firms must ensure
marketing and sales strategies formed by them is able to influence consumers to demand their
company products. Apart from attracting consumers to demand its product, marketing
strategies that formed must able to achieve company objectives and goals. In terms of
information technology, large companies are said to have better information technology
because they able to purchase expensive computers and better software. In terms of supply
chain, large companies have more suppliers because their company reputation may know by
the market participants very well. Large companies also established strong relationships with
most participants in the market. Therefore, large companies can attain many suppliers to supply
products such as raw materials used in producing their company product brand. On the other
hand, small firms will have less complex business operation because they may not involve in
many businesses. Therefore, small firms will have lesser transaction records compared to large
firms. Small firms spending on wages payroll may not as high as compared to large firms
because they hired less employees compared to large firms.
A lot of previous researchers has illustrated the relationship between size and leverage.
According to Chow (2019), to study the relationship between size and leverage in Malaysia
from 2000 to 2014, companies from 7 business sectors such as consumer, industrial,
construction, trading, real estate, agriculture, and technology has been chosen. Chow (2019)
used Balance Panel Estimation to test how size can determine leverage in Malaysia companies
that is based on 7 business sectors. The test outcome showed the relationship between size and
leverage is positive (Chow, 2019). For evidence, the coefficient for size is 0.239(Chow, 2019).
This indicates that each 1% increase in size will lead to leverage, on average, increase by
0.239% vice versa and ceteris paribus (Chow, 2019). According to Chow (2019), there is a
reason that caused the relationship between size and leverage is directly related. Big firms can
obtain borrowings or loans more easily because they are diversified (Chow, 2019). Therefore,
big firms tend to suffer higher debt and leverage since they need to repay a lot of borrowings
in future (Chow, 2019).
According to Azhari et al (2022), they had selected 348 public listed companies in
Malaysia to study the relationship between size and leverage from 2019 to 2021. 348 public
listed companies that chosen in Malaysia are taken based on 8 quarters (Azhari et al, 2022).
Azhari et al (2022) had implemented pooled ordinary least squares (OLS) estimation to test the
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CHAPTER 2: LITERATURE REVIEW
relationship between size and leverage. The result found out that size can influence leverage
positively. For evidence, the coefficient of size before Covid -19 and during Covid-19 are
0.0273 and 0.0274 respectively (Azhari et al, 2022). This implied that before Covid-19, each
1% increase in size will cause leverage, on average, increase by 0.0273% vice versa and ceteris
paribus (Azhari et al, 2022). During Covid-19, each 1% increase in size will cause leverage,
on average increase by 0.0274% vice versa and ceteris paribus (Azhari et al, 2022). The positive
relationship between size and leverage before and during Covid-19 because big firms used
borrowings from short term and long term to finance their investments projects (Azhari et al,
2022). Big firms able to demand many borrowings because they have high financial position
in their company. High company financial position can persuade lenders have to confident to
allow firms make borrowings. But there is one limitation faced by big firms which they may
suffered a higher leverage because they have a lot of unpaid borrowings (Azhari et al, 2022).
According to Yusuf et al (2013), they conducted research on how size determine leverage
in 11 electric and electronic firms in Malaysia from 1997 to 2011. Yusuf et al (2013) implement
balance panel ordinary least squares (OLS) to study the relationship between size and leverage.
The outcome is there is a direct relationship between size and leverage. For evidence, the
coefficient for size is 0.056381 which is a positive value. This inferred that each 1% increase
in size will cause leverage, on average increase by 0.056381% vice versa and ceteris paribus.
According to Yusuf et al (2013), as firms enlarge their size, they required more financial aids
to purchase new assets. Therefore, large firms will make borrowings which will increase their
debt level and leverage. The positive relationship between size and leverage in 11 electric and
electronic firms in Malaysia also consistent with trade off theory. Trade off theory stated that
the bigger the size of firm, the higher the leverage bear by firms and firms also encountered
lesser bankruptcy risk due to diversification. Since firms encounter lesser bankruptcy risk from
diversification, this allowed them can demand for borrowings. Therefore, big firms may face
higher leverage because they demand for borrowings.
According to M’ng at al (2018), they took 911 firms listed in Bursa Malaysia to examine
the relationship between size and leverage from 2004 to 2013. M’ng et al (2018) employed
pooled Ordinary Least Squares to study the relationship between size and leverage. The result
revealed that the coefficient for size is 0.0683(M’ng et al, 2017). This indicated that each 1%
increase in size will cause leverage, on average increase by 0.0683% vice versa and ceteris
paribus (M’ng et al, 2017). Therefore, it can be said that the relationship between size and
leverage is directly related. According to M’ng et al (2018), there are a few reasons why size
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CHAPTER 2: LITERATURE REVIEW
and leverage is directly related. Firstly, big companies faced fewer bankruptcy problems
because they are more diversified (M’ng et al, 2017). However, big companies may suffer
higher leverage because they borrow very much to expand their business. Second, the positive
relationship between size and leverage happened due to information asymmetry between
managers and investors (M’ng et al, 2017). Managers in the big company may have more
information about the market compared to the investor. For example, the manager may know
how they can apply loans or borrowings with a much lower interest rate payment compared to
investor (M’ng et al, 2017). Therefore, firms can apply more borrowings if they being incurred
low interest rate payments (M’ng et al, 2017). However, firms still suffered a higher leverage
because their debt level increases when they increased the borrowings (M’ng et al, 2017).
According to Ibrahim and Lau (2019), they conducted research to study the positive
relationship between size and leverage in 151 companies listed in Malaysia from 2000 to 2015.
Ibrahim and Lau (2019) employed multiple regression model to test the relationship between
size and leverage (Ibrahim & Lau, 2019). After running the test in E-view, the test outcome
showed that the coefficient of size is 0.064 which is a positive value (Ibrahim & Lau, 2019).
This implied that each 1% increase in size will cause leverage, on average increase by 0.064%
vice versa and ceteris paribus (Ibrahim & Lau, 2019). According to Ibrahim and Lau (2019),
the relationship between size and leverage is positive because large firms can apply borrowings
with lower borrowings costs such as low interest rate, therefore encourage firms borrow more
which could increase their debt level and leverage (Ibrahim & Lau, 2019). Secondly, large
firms are easier to get borrowings compared to small firms because large firms may
successfully establish good relationship among the market players (Ibrahim & Lau, 2019). This
caused large firms can negotiate for a better loan contract such as getting higher borrowed
funds with a longer repayment period. Hence, large firms may suffer higher leverage because
they need to repay their borrowings which is huge in amount (Ibrahim & Lau, 2019).
On the other hand, some researchers argue that the relationship between size and
leverage should be negative. According to Marimuthu and Hamzah (2020), they conducted a
study to analyze the relationship between size and leverage in 28 oil and gas firms in Malaysia
from 2008 until 2017. Data about the 28 oil and gas companies in Malaysia was extracted from
annual reports or financial statements in Bursa Malaysia (Marimuthu & Hamzah, 2020).
Marimuthu and Hamzah (2020) implemented Levin-Lin Chu test to study the relationship
between size and leverage from 28 oil and gas companies in Malaysia. The result showed that
the coefficient for size is -19.4718 which is a negative value (Marimuthu & Hamzah, 2020).
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CHAPTER 2: LITERATURE REVIEW
This negative value showed that the relationship between size and leverage is inversely related.
For evidence, for each 1% rise in size, it will lead to leverage, on average drop by 19.4718%
vice versa and ceteris paribus (Marimuthu & Hamzah, 2020). According to Marimuthu and
Hamzah (2020), there is one reason that caused inverse relationship between size and leverage.
The reason is large firms can finance their projects using internal funds. So, they do not need
to make a lot of borrowings (Marimuthu & Hamzah, 2020). Therefore, big firms can be said
suffered lesser leverage because they bear fewer borrowings debt (Marimuthu & Hamzah,
2020).
According to Saarani and Shahadan(2013), they conducted a research to examine the
relationship between size and leverage in Malaysia from 2005 to 2009. Saarani and
Shahadan(2013) had selected 334 companies in Malaysia to study the relationship between size
and leverage. Pooled Ordinary Least Squares (OLS) Estimation had used to test the relationship
between size and leverage (Saarani & Shahadan, 2013). The finding is the coefficient for size
is -0.033 which is a negative value (Saarani & Shahadan, 2013). This implied that each 1%
increase in size will cause leverage, on average, decrease by 0.033% vice versa and ceteris
paribus (Saarani & Shahadan, 2013). So, the relationship between size and leverage is
negatively related. According to Saarani and Shahadan(2013), short term borrowings is the
factor that caused the negative relationships between size and leverage(Saarani & Shahadan,
2013). Big firms may face time constraint to repay borrowings which is due in short term.
Therefore, in order to repay borrowings in short term, big firms may repay the borrowings by
using internal funding. In that case, big firms leverage dropped because their debt burden had
reduced after repaying borrowings (Saarani & Shahadan, 2013).
The authors such as Cortez and Susanto (2012) found out that there is a negative
relationship between size and leverage. Cortez and Susanto (2012) conducted research to
examine the relationship between size and leverage by taking 21 well-known firms in Japan
that listed in Tokyo Stock Exchange during 2001 to 2010. Cortez and Susanto (2012) employed
pooled Ordinary Least Squares to test the relationship between size and leverage. The result
found out that the coefficient for size is -0.0062695 which is a negative value (Cortez &
Susanto, 2012). This implied that each 1% increase in size will cause leverage, on average
decrease by 0.0062695% vice versa and ceteris paribus (Cortez & Susanto, 2012). Therefore,
size is negatively determining leverage in 21 Japanese firms. Cortez and Susanto (2012) argue
that the negative relationship between size and leverage is influenced by pecking order theory
(Cortez & Susanto, 2012). According to pecking order theory, companies have lesser
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CHAPTER 2: LITERATURE REVIEW
information asymmetry compared to investor (Cortez & Susanto, 2012). Therefore, companies
know very well how to attain lower cost of borrowings. Therefore, companies can issue higher
equity to finance their business activities (Cortez & Susanto, 2012)
According to Vatavu(2013), he conducted a study to analyze the relationship exist
between size and leverage in Romania from 2003 to 2012. To conduct this research, the author
had collected 196 manufacturing firms that listed in Bucharest Stock Exchange (Vatavu, 2013).
Vatavu (2013) employed balance panel Ordinary Least Squares (OLS) regression to examine
the relationship between size and leverage in 196 Roman manufacturing companies (Vatavu,
2013). The result found out that the coefficient for size is -0.0008 (Vatavu, 2013). This implied
that each 1% increase in size will cause leverage, on average, reduce by 0.0008% vice versa
and ceteris paribus. So, this indicated that there is a negative relationship between size and
leverage. Vatavu (2013) said that the negative relationship between size and leverage is caused
by borrowings repayment which will due in short term. Since small firms faced a lot of agency
problems issue, they discouraged from undertake long term borrowings. Secondly, firms may
apply for short term borrowings to eliminate their cash flow problems. If companies do not
apply for short term borrowings to solve their cash flow problem, they may encounter higher
bankruptcy risk. Higher bankruptcy risk may cause companies fail to repay current and future
borrowings. Therefore, company leverage will increase because a lot of borrowings are unpaid
(Vatavu, 2013).
standardize the margin According
to Bhaduri(2010), he selected 363 companies from Centre for Monitoring
Indian Economy(CMIE) to examine the relationship between size and leverage from 1990 to
1995. 363 companies that selected in the research are taken from manufacturing sector in India.
Bhaduri(2010) employed pooled Ordinary Least Squares (OLS) estimation to test the
relationship between size and leverage. The result found out that the coefficient for size is 0.003621. This indicated that each 1% increase in size will cause leverage on average reduce
by 0.003621% vice versa and ceteris paribus. Therefore, it can be said that the relationship
between size and leverage is negatively related. According to Bhaduri(2010), there is a reason
that cause the negative relationship between size and leverage. Big firms encounter fewer
financial issues because they are more diversified. Therefore, firms can increase their
borrowings because they able to repay borrowings demand by them. As big firms increase their
borrowings, firms encountered a higher debt and leverage because firms need to repay these
borrowings in future.
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CHAPTER 2: LITERATURE REVIEW
In conclusion, there is a contradiction among researchers regarding the relationship
between size and leverage. For example, some researchers argue that the relationship between
size and leverage should be positive. Whereas, some researchers argue that the relationship
between size and leverage is negative. The authors had illustrated the reasons about the positive
or negative relationship that exist between size and leverage.
2.3.3 Growth and leverage
According to Ibrahim and Lau (2019), growth is defined as changes that occurred in
firms’ total assets (Ibrahim & Lau, 2019). The public can know about the changes in the firms’
total assets by viewing their annual reports that published in company own website or Bursa
Malaysia (Ibrahim & Lau, 2019). The investors also can decide whether suitable to invest in
the company or not after reviewing their annual report (Ibrahim & Lau, 2019). For example, if
the firms’ total assets drop, this indicates that firm’s growth was drop. This signal investors not
the invest in the company. However, if the firms’ total assets value increase, this indicates that
firms’ growth rises. Thus, the investor can take consideration whether can invest in this
company or not. Company growth will influence investors profits. For example, if the company
growth is higher, dividend received by each investor also higher. In that case, investors are
benefited from the investment because investors gain larger profits from investments. On the
other hand, if the company growth reduces, dividend received by investors may reduce.
Therefore, investors are worse off from investments because they received a lower dividend.
In that case, investors should seek for companies that achieved better growth annually to help
them benefited from investments. Growth also is a determination of company progress. Higher
assets value indicates a higher growth whereas lower assets value indicates company growth
decreased. If the companies achieve a higher growth, it can be said the marketing strategies
used by the company is correct. Therefore, company can continue follow used this marketing
strategies in future. If the company assets value is low, the company need to find out the
problems that caused lower assets value such as reviewing marketing strategies to see whether
strategies used for marketing is accurate or not. After that, company need to implement new
marketing strategy to ensure company growth can increase.
A lot of previous researchers had conducted study about the relationship between growth
and leverage. According to Ibrahim and Lau (2019), he conducted a study regarding the
relationship between growth and leverage in Malaysia from 2000 to 2015. Ibrahim and Lau
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CHAPTER 2: LITERATURE REVIEW
(2019) selected 151 companies listed in Malaysia in order to examine the relationship between
growth and leverage in Malaysia. Ibrahim and Lau (2019) employed multiple regression model
to study the relationship between growth and leverage in Malaysia. In multiple regression
model, the variable growth is regressed as an independent variable whereas the variable
leverage is regressed as a dependent variable (Ibrahim & Lau, 2019). The findings revealed
that the coefficient for growth is 0.0001(Ibrahim & Lau, 2019). This indicated that each 1%
increase in growth will lead to leverage, on average increase by 0.0001% vice versa and ceteris
paribus. Therefore, the relationship between growth and leverage is positively related.
According to Ibrahim and Lau (2019), the positive relationship between growth and leverage
is influenced by pecking order theory. Pecking order theory stated that when firms’ growth is
higher, firms will demand more borrowings (Ibrahim & Lau, 2019). As firms demand on
borrowings increase, firms will bear increasing debt level and leverage (Ibrahim & Lau, 2019).
According to Rus and Samiran (2012), they conducted a study to analyse the
relationship between growth and leverage in Malaysia from 2003 to 2008. Rus and Samiran
(2012) had selected 37 construction companies to conduct this research. To test the relationship
between growth and leverage, pooled Ordinary Least Square (OLS) had employed. The result
is the relationship between growth and leverage is positively related. For evidence, the
coefficient for growth is 0.653. This implied that each 1% increase in growth will cause
leverage on average increase by 0.653% vice versa and ceteris paribus. There are some reasons
that caused the positive relationship between growth and leverage. Firstly, firms will encounter
lesser leverage during short run. The reason is firms unable to increase borrowings demand due
to the short time. During long run, firms may face a larger leverage. The reason is firms got
more time to seek for more borrowing’s opportunity in the market, thus firms’ borrowings will
increase as well as leverage. Secondly, firms are easy to apply for short term borrowings in
Malaysia. The reason is credit market in Malaysia is more easy access by firms. Therefore,
firms can apply more short-term borrowings which will increase their leverage and debt level.
Another researcher, Barclay and Smith (2005) stated that growing firms may demand more
short-term borrowings rather than long term borrowings. The reason is growing firms want to
avoid themselves suffered from higher leverage and debt level. Since growing firms only
demand short term borrowings, their leverage and debt level will be lesser. Therefore, the
relationship between growth and leverage is positively related.
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CHAPTER 2: LITERATURE REVIEW
According to Chow (2019), he conducted research to examine the relationship between
growth and leverage in Malaysia from 2000 to 2014. Chow (2019) selected companies that
based on 7 sectors in Bursa Malaysia such as consumer, industrial, construction, trading, real
estate, plantation, and technology. In order to test the relationship between growth and
leverage, pooled Ordinary Least Squares (OLS) had employed. The result showed that the
relationship between growth and leverage is directly related. For evidence, the coefficient for
growth is 0.222 (Chow, 2019). This implied that each 1% increase in growth will cause
leverage on average increase by 0.222% vice versa and ceteris paribus. There are some reasons
that caused the positive relationship between growth and leverage. Firstly, companies need
larger fund to finance their investment opportunities (Chow, 2019). Investment opportunities
may increase firms’ growth in the industry. Therefore, firms will rise demand on borrowings
to stimulate their investment growth but firms may end up with higher leverage. Firms will
encounter higher leverage because they need to repay huge borrowings in future. Hence, the
relationship between growth and leverage is positively related (Chow, 2019).
According to Azhari et al (2022), they conducted a study to examine the relationship
between growth and leverage in Malaysia from 2019 to 2021. Azhari et al (2022) had chosen
348 public listed firms in Malaysia to examine the relationship between growth and leverage.
In order to study the relationship between growth and leverage, pooled Ordinary Least Square
had employed (Azhari et al, 2022). The findings found out that the relationship between growth
and leverage is positively related. For evidence, the coefficient for growth before Covid-19
(2019) is 0.0202(Azhari et al, 2022). This implied that before Covid-19, each 1% increase in
growth will lead to the leverage on average increase by 0.0202% (Azhari at al, 2022). However,
the coefficient for growth during Covid-19 is 0.0076(Azhari at al, 2022). This implied that
during Covid-19, each 1% increase in growth will lead to the leverage, on average increase by
0.0076% (Azhari et al, 2022). There are some reasons that bring into the positive relationship
between growth and leverage before Covid-19 and during Covid-19 in Malaysia. Before Covid19 which is during year 2019, most firms are more willing to take risk for investment because
they are confident about the future economy growth (Azhari et al, 2022). Therefore, most firms
will borrow large amount of money to finance for investment opportunities and this could
strengthen their growth (Azhari et al, 2022). In that case, since firms borrow large amount of
money, this would increase their leverage and debt level. During Covid-19 which is in 2020
and 2021, firms does not have confident about the future economy growth. Therefore, firms
will not demand for any borrowings because firms have no intention to perform investment
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CHAPTER 2: LITERATURE REVIEW
activities (Azhari et al, 2022). Since firms does not make any borrowings, firms will not suffer
any leverage and debt and at the same time firms may not show any growth since they do not
involve in investment activity (Azhari at al, 2022). Hence, the positive relationship between
growth and leverage is being established.
According to Mustapha et al (2011), they conducted research to examine the
relationship between growth and leverage in Malaysia from 2000 to 2021. Mustapha et al
(2011) had selected 235 public listed companies in Bursa Malaysia to study the relationship
between size and leverage in Malaysia. Mustapha et al (2011) focussed on how growth can
determine leverage from different economy sectors in Malaysia. Mustapha et al (2011)
implemented balance panel Ordinary Least Square to study the relationship between growth
and leverage in Malaysia. In consumer aspect, the coefficient for growth is 0.182 and this
indicated that each 1% increase in growth will cause leverage on average increase by 0.182%.
According to Mustapha et al (2011), firms made a lot of borrowings to finance for goods and
services production that need by consumers. Thus, this cause firm’s growth to increase because
firms gain many sales from producing goods need by consumer. In construction aspect, the
coefficient for growth is 0.263 and this indicated that each 1% increase in growth will cause
leverage, on average increase by 0.263%. Firms may need huge funding to finance their
construction cost. For example, firm will demand borrowings to finance cost of building new
factory. Building of new factory can influence firms’ growth because more goods can supply
to fulfil consumer demand. Firms may end up with higher leverage because they need to repay
the borrowings in future. In trading aspect, the coefficient for growth is 0.179 which indicated
that each 1% increase in growth will lead to leverage, on average, increase by 0.179%. Firms
may need to set up shops to sell their goods. The more the shops firms intend to set up to sell
their cost, the higher the cost of setting shops faced by firms. Therefore, firms may try to borrow
to finance their cost of setting up shops. As firms intend to make borrowings to finance their
cost of setting shops, firms will face a higher leverage and debt level. On the other side, firms
may grab larger sales because firms set up a lot of shops to sell their goods. Larger sales give
firms a higher growth opportunity. Based on the author study, the author found out that growth
and leverage is positively related in aspect of consumer, construction, and trading. Hence, the
relationship between growth and leverage is positive.
On the other hand, some researchers argue that the relationship between growth and
leverage should be inversely related. According to Saarani and Shahadan (2013), they
conducted a study to observe the relationship between size and leverage in Malaysia from 2005
35
CHAPTER 2: LITERATURE REVIEW
to 2009. In order to study the relationship between size and leverage, Saarani and
Shahadan(2013) had selected 334 small and medium size enterprises(SMEs) in Malaysia.
Saarani and Shahadan(2013) used pooled ordinary least square(OLS) to test the relationship
between size and leverage. The result revealed that the coefficient for growth is -0.202. This
indicated that each 1% increase in growth will cause leverage, on average decrease by 0.202%.
Growing firms will not rely on borrowings to finance their investment activities because they
can finance investments activities by using company funds. Thus, when growing firms used
internal funds to finance their investment activities, they will not suffer any leverage and debt
because they did not demand for any borrowings.
According to Ibrahim and Lau (2019), they conducted research to analyse the relationship
between growth and leverage in Malaysia from 2000 to 2015. Ibrahim and Lau (2019) had
taken 151 companies from Bursa Malaysia to study the relationship between growth and
leverage. Ibrahim and Lau (2019) employed multiple regression to analyse the relationship
between growth and leverage. The result showed that the coefficient for growth is -0.00006
and this showed that each 1% increase in growth will cause leverage, on average to decline by
0.00006% (Ibrahim & Lau, 2019). According to Ibrahim and Lau (2019), the cause of negative
relationship between growth and leverage is short term debt (Ibrahim & Lau, 2019). Short term
debt is known as firms’ borrowings that may due in short period of time (Ibrahim & Lau, 2019).
Due to short period of borrowings repayments, growing firms may unable to grab enough sales
internally to repay their borrowings (Ibrahim & Lau, 2019). Therefore, due to a lot of unpaid
borrowings, the borrowers may incur penalty to the company such as imposing extra charges
of interest rate payment to force the company settle their borrowings repayment immediately
(Ibrahim & Lau, 2019). This will cause firms leverage increases because firms suffer a larger
borrowings repayment. Hence, Ibrahim and Lau (2019) illustrated the relationship between
growth and leverage is inversely related.
However, author from other country also do research regarding the relationship between
growth and leverage. According to Cortez and Susanto (2012), they studied the relationship
between growth and leverage in Japan from 2001 to 2010. Cortez and Susanto (2012) selected
21 Japanese manufacturing firms that listed in Tokyo Stock Exchange. Cortez and Susanto
(2012) implemented balance panel regression to test the relationship between growth and
leverage. The result revealed that the coefficient for growth is -0.3138373 and this indicated
that each 1% increase in growth will cause leverage, on average drop by 0.3138373% (Cortez
& Susanto, 2012). Therefore, this indicated that the relationship between growth and leverage
36
CHAPTER 2: LITERATURE REVIEW
is negatively related. The cause of the negative relationship between growth and leverage is
long term debt. Even though growing firms given longer borrowings repayment period, they
still bear higher debt level unless successfully settle all the debt (Cortez & Susanto, 2012).
Therefore, growing companies may suffer larger leverage due to a lot of unpaid borrowings
(Cortez & Susanto, 2012).
The study conducted by the author in Vietnam also found that the relationship between
growth and leverage is negatively related. According to Do (2021), to analyse the relationship
between growth and leverage in Vietnam from 2009 to 2019, 78 consumer goods firms had
been selected. Do (2021) employed pooled ordinary least square (OLS) regression to study the
relationship between growth and leverage. The research found out that the relationship between
growth and leverage is negatively related. For evidence, the coefficient for growth is -0.00001
and this indicated that each 1% increase in growth will cause leverage on average decreased
by 0.00001% (Do, 2021). The negative relationship that exists between growth and leverage is
influenced by market timing theory (Do, 2021). According to market timing theory, in short
period of time, firms unable to seek for much source of borrowings in the market. Therefore,
firms’ borrowings are lesser as well as leverage (Do, 2021).
Next, the study conducted by a group of researchers in South Korea found out that the
relationship between growth and leverage is negatively related. According to Choi,Yoo,Kim
and Kim(2018), they conducted a research to analyse the relationship between growth and
leverage in 43 companies in South Korea from 2000 to 2010. Choi at al (2018) employed
multiple regression model to test the relationship between growth and leverage. The findings
revealed that the relationship between growth and leverage is negatively related. For evidence,
the coefficient for growth is -0.117 and this indicated that each 1% increase in growth will
cause leverage, on average reduce by 0.117% vice versa and ceteris paribus (Choi et al, 2018).
Companies with larger growth should invest in investment that can maximise shareholder
profits (Choi et al, 2018). The reason is if the higher growth companies invest in an unprofitable
investment, this may end up the higher growth company suffered losses from investments and
may end up borrow money to cover their losses from investments (Choi et al, 2018). When
high growth companies demand many borrowings to cover their losses, they may encounter
higher leverage and debt (Choi at al, 2018).
In conclusion, there is a contradiction study among researchers regarding the relationship
between growth and leverage. Company growth is crucial for a company because company that
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CHAPTER 2: LITERATURE REVIEW
possess good growth can maintain a good ranking in the industry. Therefore, firms in all
industries must put emphasis on keeping a good growth.
2.3.4 Profitability and leverage
Profitability can measure how firms used their assets to manage their business operation
(Jaafar et al, 2017). For example, when firms used capital to produce goods, firms can produce
goods with a lesser cost of production. With a lesser cost of production, this encourages firms
to increase goods productivity. Since firms can produce more goods and services, firms can
supply more goods to fulfil the demand in the market. Therefore, firms can gain higher sales
in the market. Higher sales will increase a firm profit in the market. On the other side, if the
assets used by firms to produce goods and services is obsolete, this unable to aid firms compete
for a higher profit in the market. The reason is obsolete assets are less effective to reduce cost
of production compared to new assets. Therefore, firms that used obsolete assets to produce
goods is unable to produce goods as much as firms that used new assets. Since firms’
production is lower because they used obsolete assets to produce goods and services, therefore
they unable to supply much goods to fulfil demand in the market. This eventually lead firms
gain a lower sale in the market. If firms gain lower sales in the market, profits gain by firms
will become lower. Profitability also is a measurement of company success and failure (Jaafar
et al, 2017). If the company can gain higher profits in the industry, then, this indicate the
company is success in the industry. If company gain lower profits in the industry until make
losses, in that case, the company is considered failure. Profitability also influences firms when
they make borrowings. Lenders may review company profits through their annual report before
decide to lend them money (Jaafar et al, 2017). Lenders may not decide to lend money to the
company that making losses in the business because they worried companies may unable to
repay the borrowings. Lenders would be more preferred to lend money to the company that
make high profits. Companies that make higher profits have higher capability to repay their
borrowings compared to companies that making lower profits and losses in the. If companies
choose to delay their borrowing repayments, this cause companies to suffer a higher debt and
leverage.
A lot of previous researchers had conducted study to examine the relationship between
profitability and leverage. According to Jaafar et al (2017), they conducted research to examine
the relationship between profitability and leverage in Malaysia from 2006 to 2016. Jaafar et al
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CHAPTER 2: LITERATURE REVIEW
(2017) had chosen 34 shariah firms from Bursa Malaysia and Ace Market to study the
relationship between profitability and leverage in Malaysia. Jaafar et al (2017) employed
multiple regression model to examine the relationship between profitability and leverage. In
multiple regression model, the variables such as profitability, tangibility and liquidity regressed
as independent variables whereas the variable which is debt ratio regressed as dependent
variable (Jaafar et al, 2017). The findings found out that there is a positive relationship between
profitability and leverage (Jaafar et al, 2017). For evidence, the coefficient for profitability is
0.286 and this indicated that each increase of RM1 in profitability will lead to leverage on
average to increase by RM0.286 vice versa and ceteris paribus (Jaafar et al, 2017). According
to Jaafar et al (2017), the positive relationship between profitability and leverage is influenced
by trade off theory (Jaafar et al, 2017). According to trade off theory, high profits firms should
take more borrowings compared to low profits firms. The reason is company with higher profits
are benefited from demanding borrowings because they will receive a reduction on interest rate
payments from borrowings (Jaafar et al, 2017). Since high profits firms increase their
borrowings due to reduction on interest rate payments, high profits firms may suffer higher
leverage because they need to repay a lot of borrowings in future (Jaafar et al, 2017). On the
other side, low profits firms are considered worse off if they demand for borrowings because
they be incurred higher interest rate from borrowings. This will end up low profits firms need
to repay huge borrowings in future because higher interest rate payment had increased
borrowings repayments in future.
According to Yusuf et al (2013), they conducted research to examine the relationship
between profitability and leverage in Malaysia from 1997 to 2011. Yusuf et al (2013) had
selected 11 electric and electronic companies in Malaysia to study the relationship between
profitability and leverage. Yusuf et al (2013) employed balance panel ordinary least square
(OLS) to examine the relationship between profitability and leverage. The findings revealed
that there is a positive relationship between profitability and leverage. For evidence, the
coefficient for profitability is 0.014191 which indicated that each increase of RM1 in profit
will cause the leverage, on average to increase by RM0.014191 vice versa and ceteris paribus
(Yusuf et al, 2013). The author stated that the positive relationship between profitability and
leverage is influenced by trade off theory (Yusuf et al, 2013). Trade off theory stated that high
profit companies are more encourage to apply more borrowings because taxes charges on
interest rate payment on borrowings is lesser (Yusuf et al, 2013). Since high profits firms
demand more borrowings, their leverage will increase. Therefore, trade off theory illustrated
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CHAPTER 2: LITERATURE REVIEW
the relationship between profitability and leverage should be directly related (Yusuf et al,
2013).
Research done by authors from the other countries also found that there is a positive
relationship between profitability and leverage. According to M’ng et al (2018), they conducted
a study to analyse the relationship between profitability and leverage in Thailand from 2004 to
2013. M’ng et al (2018) had selected a total of 280 non-financial companies from Thailand
Stock Exchange to analyse the relationship between profitability and leverage. Type of data
chosen by M’ng et al (2018) to conduct this research is balance panel data. In this research, the
author had employed fixed effect ordinary least squares (OLS) regression to test the
relationship between profitability and leverage (M’ng et al, 2017). The findings showed that
there is a direct relationship between profitability and leverage. For evidence, the coefficient
for profitability is 0.0053 and this indicated that each increase of 1 Thai Baht in profitability
will cause leverage, on average increase by 0.0053 Thai Baht (M’ng et al, 2017). The author
stated that the positive relationship between profitability and leverage is cause by trade off
theory. According to trade off theory, high profits firms will face lesser bankruptcy risk because
profits earned by them is higher (M’ng et al, 2017). Therefore, high profits firms can apply for
many borrowings in capital market. However, as high profit firms apply for many borrowings,
they may suffer higher leverage because need to repay many borrowings in future (M’ng et al,
2017). On the other side, low profits firms will face higher bankruptcy risk because the profits
earned by them is too low until unable to cover their losses in business (M’ng et al, 2017).
Therefore, low profits firms are unable to demand for borrowings (M’ng et al, 2017). Since
low profits firms unable to demand borrowings, therefore, they will not suffer any leverage
(M’ng et al, 2017).
According to Tarazi (2013), he conducted a study to analyse the relationship between
profitability and leverage in Thailand from 2001 to 2011. The author had selected 559
companies that listed from Thailand stock market to study the relationship between profitability
and leverage. The author also employed multiple regression to test the relationship between
profitability and leverage. In multiple regression, the variables such as profitability, size,
growth and tangibility regressed as independent variables whereas the variable which is
leverage regressed as dependent variable. The result found out that there is a positive
relationship between profitability and leverage. For evidence, the coefficient for profitability
is 0.031186 and this indicated that each increase of 1 Thai Baht in profitability will cause
leverage, on average, increase by 0.031186 Thai Baht. According to Tarazi (2013), the positive
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CHAPTER 2: LITERATURE REVIEW
relationship between profitability and leverage is influenced by static trade-off theory.
According to static trade off theory, firms that earn bigger profits are more encourage to
demand more borrowings because they have higher capability to repay borrowings. Since high
profit firms have higher ability to repay borrowings, creditors and lenders are benefited because
money that lend out can repay successfully. High profit company is also benefited because their
debt level and leverage being reduce as they succeed settle borrowings repayments.
According to Alkhatib (2012), he conducted a study to analyse the relationship between
profitability and leverage in Jordan from 2007 to 2010. The author had selected a sample of
121 companies from industry and service sectors from Jordanian Stock Exchange to conduct
this research. The author employed multiple regression model to test the relationship between
profitability and leverage. In multiple regression model, the variables such as profitability,
tangibility, growth, size and liquidity regressed as independent variables whereas the variable
which is leverage regressed as dependent variable. The findings revealed that there is a positive
relationship established between profitability and leverage. For evidence, the coefficient for
profitability is 0.004 and this indicated that each increase of 1 Jordanian Dinar will cause the
leverage, on average increase by 0.004 Jordanian Dinar. There are a few reasons that cause the
positive relationship between profitability and leverage. Firstly, high profit firms only required
to repay small amount of interest rate payment on borrowings. Since their interest rate
payments on borrowings is lower, therefore, high profits firms will bear lesser cost of
borrowings. Therefore, high profits firms will suffer a lower leverage. Secondly, high profits
companies will face less information asymmetry in the market. High profits firms can attain
any updates in the market information more easily because they established good rapport with
several market participants. For example, high profit firms can know where they can demand
borrowings with a lower interest rate payment. Therefore, high profit firms will bear fewer
leverage as their borrowing’s repayment is lower. Hence, the author illustrated that there should
be a direct relationship between profitability and leverage.
On the other hand, some former researchers commented that the relationship between
profitability and leverage should be negatively related. According to Rus and Samiran (2012),
they conducted research to examine the relationship between profitability and leverage in
Malaysia from 2003 to 2008. The author had selected 37 construction companies from Bursa
Malaysia to examine the relationship between profitability and leverage (Rus & Samiran,
2012). The author employed Balance Panel Ordinary Least Square (OLS) regression to test the
relationship between profitability and leverage (Rus & Samiran, 2012). After the author test
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CHAPTER 2: LITERATURE REVIEW
the relationship between profitability by using Balance Panel Ordinary Least Square, the author
found out that the relationship between profitability and leverage is inversely related (Rus &
Samiran, 2012). For evidence, the coefficient value for profitability is -0.374 and this indicated
that each increase of RM1 in profitability will cause the leverage, on average decrease by
RM0.374 vice versa and ceteris paribus (Rus & Samiran, 2012). According to Rajan and
Zingales (1995), when high profits companies expand their size, high profits firms will increase
the demand of borrowings. The reason is internal financing that available in high profits
companies is unable to provide sufficient funding to finance their size expansion costs (Rus &
Samiran, 2012). Therefore, as high profits firms demand borrowings to finance the cost of
expanding their company, this will end up high profits’ companies will bear a larger leverage
because they suffer huge borrowings repayments (Rus & Samiran, 2012).
Another research done by previous researchers in Pakistan found out that the
relationship between profitability and leverage is negatively related. According to Shah and
Hijazi (2004), they conducted a study to examine whether the relationship between profitability
and leverage in Pakistan is negatively related from 1997 to 2001. The author had selected a
total of 445 companies from Karachi Stock Exchange to analyse the relationship between
profitability and leverage (Shah & Hijazi, 2004). 445 companies that selected to conduct this
research is not come from financial sector (Shah & Hijazi, 2004). The author implemented
balance panel ordinary least square (OLS) to test the relationship between profitability and
leverage (Shah & Hijazi, 2004). The findings showed that profitability is negatively influence
leverage. For evidence, the coefficient for profitability is -1.1069 and this indicated that each
increase of RM1 in profitability will cause leverage, on average, reduce by RM1.1069 vice
versa and ceteris paribus (Shah & Hijazi, 2004). According to Shah and Hijazi, the negative
relationship between profitability and leverage is influenced by pecking order theory (Shah &
Hijazi, 2004). According to pecking order theory, high profits firms will choose to finance their
business costs by using internal funds rather than borrowings (Shah & Hijazi, 2004). High
profits firms only borrow if the internal funding is insufficient to cover their business costs
(Shah & Hijazi, 2004). Therefore, pecking order theory argue that high profits firms will suffer
fewer leverage. Hence, the negative relationship between profitability and leverage is
successfully established.
According to Chow (2019), he conducted research to study the relationship between
profitability and leverage in Malaysia from 2000 to 2014(Chow, 2019). Companies that
selected in this research are taken from 7 sectors in Malaysia. These 7 sectors include
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CHAPTER 2: LITERATURE REVIEW
consumer, industry, constructions, trading, real estate, agriculture, and technology (Chow,
2019). Chow (2019) implemented pooled ordinary least square (OLS) to study the relationship
between profitability and leverage. The findings found out that there is an inverse relationship
between profitability and leverage. For evidence, the coefficient for profitability is -1.440 and
this indicated that each increase of RM1 in profitability will cause the leverage, on average,
drop by RM1.440 vice versa and ceteris paribus (Chow, 2019). According to Chow (2019), the
negative relationship between profitability and leverage is caused by pecking order theory.
Pecking order theory stated that high profits company prefer to use internal earnings to finance
their investment project rather than borrowings (Chow, 2019). This happens due to information
asymmetry problems in the market. Due to information asymmetry, high profits firms need to
buy information that available in the market (Chow, 2019). Sometimes, high profits firms may
not willing to spend money on purchasing information because this will increase their business
expenditure. For example, even though high profits firms can buy information on where they
can get much borrowings with cheaper interest rate payment, sometimes they may not willing
to purchase this information to avoid rising in business expenses. Since high profits firms use
internal finance to finance their business, leverage bear by them will be lesser because they do
not demand for borrowings. Hence, it can be said that the relationship between profitability and
leverage is inversely related.
According to Ibrahim and Lau (2019), they conducted study to analyse the relationship
between profitability and leverage in Malaysia from 2000 to 2015. The author had selected 151
public listed companies that taken from Bursa Malaysia to examine the relationship between
profitability and leverage (Ibrahim & Lau, 2019). The author employed multiple regression to
test the relationship between profitability and leverage. In the multiple regression, the variables
tangibility, profitability, size, growth, and liquidity regressed as independent variable whereas
the variable leverage regressed as dependent variable (Ibrahim & Lau, 2019). The findings
found out that profitability is negatively influence leverage. For evidence, the coefficient for
profitability is -0.231 and this indicated that each RM1 increase in profitability will cause
leverage, on average reduce by RM0.231 vice versa and ceteris paribus (Ibrahim & Lau, 2019).
The author stated that the negative relationship between profitability and leverage is caused by
pecking order theory (Ibrahim & Lau, 2019). Pecking order theory stated that profitable
companies will fund their business by using their own business earnings rather than borrowings
(Ibrahim & Lau, 2019). Therefore, profitable companies would not suffer any leverage because
43
CHAPTER 2: LITERATURE REVIEW
they do not demand on borrowings (Ibrahim & Lau, 2019). Hence, the author proposed that the
relationship between profitability and leverage is negative (Ibrahim & Lau, 2019).
According to Cortez and Susanto (2012), they conducted research to examine the
relationship between profitability and leverage in Japan from 2001 to 2010. The author had
chosen 21 manufacturing firms that listed in Tokyo Stock Exchange to study the relationship
between profitability and leverage (Cortez & Susanto, 2012). The author used balance panel
ordinary least square regression (OLS) to test the relationship between profitability and
leverage (Cortez & Susanto, 2012). The findings found out that there is an inverse relationship
between profitability and leverage (Cortez & Susanto, 2012). For evidence, the coefficient of
profitability is -1.110607 and this indicated that each increase of RM1 in profitability will cause
leverage, on average reduce by RM1.110607 vice versa and ceteris paribus (Cortez & Susanto,
2012). The author said that pecking order theory is the cause of negative relationship between
profitability and leverage (Cortez & Susanto, 2012). This author findings are consistent with
other researchers such as Shah and Hijazi (2004), Chow (2019), Ibrahim and Lau (2019) and
Cortez and Susanto (2012). According to pecking order theory, profitable firms will finance
their company business expenditures by using internal funding rather than borrowings (Cortez
& Susanto, 2012). The reason is profitable companies can avoid suffered higher borrowings
repayments debts if they choose not to borrow (Cortez & Susanto, 2012). As profitable firms
do not borrow, they will suffer lesser leverage (Cortez & Susanto, 2012). Hence, the author
illustrated the relationship between profitability and leverage is inversely related (Cortez &
Susanto, 2012).
In conclusion, there is a contradiction finding among researchers regarding the
relationship between profitability and leverage. In order to achieve higher profitability, apart
from having accurate marketing strategies, firms can also involve in corporate social
responsibility such as donating funds to aid poor people in the society. Involvement in
corporate social responsibility can help a company gain a good image from the society.
Therefore, consumers may support the company such as purchasing the company goods in
bulk. This can help the company gain higher sales and profits in the industry. If firms can gain
higher profits, they may have enough funding to support their investments. Thus, firms can
avoid themselves from continue taking borrowings which will increase their leverage.
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CHAPTER 3: METHODOLOGY
3.1 Introduction
The aim of this research is to observe how tangibility, size, growth, and profitability can
determine leverage in Malaysia. In this chapter, we will create economic model definition
based on former studies. In this chapter, we also focus on sources, measurements, and
descriptive statistics for all variable. Next, we will also form the expected sign for each variable
and each variable formed by us are based on the theoretical framework from chapter 2. Besides
that, we also describe econometrics method used in this research such as panel regression and
many diagnostic test methods. This chapter concluded where we need to form hypothesis to
analyse how the independent variables able to determine dependent variable.
3.2 Economic Model Specification
explain also why
the 3 companies
have been
chosen for your
study in this subsection
Trade off theory and pecking order theory illustrated the relationship between leverage,
tangibility, size, growth, and profitability. Firstly, we will explore the relationship between
tangibility and leverage. According to trade off theory, leverage is depended on tangibility. If
firms are given longer time to repay their borrowings, firms may have enough time to decide
on how much tangible assets need to sell for borrowings repayments (Ibrahim & Lau, 2019).
As firms sell their tangible assets to repay borrowings, tangible assets own by firms will reduce
and at the same time firms will bear lesser leverage because borrowings repayment had reduced
(Ibrahim & Lau, 2019). Apart from that, tangible assets can act as collateral for borrowings
repayments. Firms can use their tangible assets such as machinery and equipment and factory
to serve as a collateral for when making borrowings. If firms failed to repay borrowings to
creditors, creditors can claim tangible assets from firms. Thus, this will reduce firms’ tangible
assets and leverage at the same time. Second, leverage is also relied on size. According to trade
off theory, the bigger the size of firms, the larger the leverage bear by firms (Yusuf et al, 2013).
Bigger firms will encounter lesser bankruptcy risk because they got diversified the risk of
bankruptcy (Yusuf et al, 2013). Therefore, this allowed big firms can apply more borrowings
which will increase their leverage (Yusuf et al, 2013). So, as firm size increase, leverage will
increase. Thirdly, growth of firms is also depended on leverage. According to pecking order
theory, growing firms will face larger leverage (Ibrahim & Lau, 2019). Growing firms need
borrowings to help them expand their business (Ibrahim & Lau, 2019). Thus, as growing firms
demand for borrowings, they will face larger leverage (Ibrahim & Lau, 2019). Lastly, leverage
also depend on profitability. According to pecking order theory, profitable firms will only
demand borrowings if their internal funding is not sufficient to finance their business (Shah &
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CHAPTER 3: METHODOLOGY
Hijazi, 2004). As profitable firms demand borrowings, they will suffer a higher leverage (Shah
& Hijazi, 2004). Therefore, we can express the model as:
LEVi,t = α0 + α1 TANGi,t +α2 SIi,t +α3 GRi,t +α4 PROi,t +ᶓi,t
(1)
Where: LEV = leverage in Malaysia
TANG = Tangibility in Malaysia
SI = Size in Malaysia
GR = growth in Malaysia
PRO = profitability in Malaysia
i,t =
Cross section Annual Time Period from 1999 to 2021
α0 = Constant
ᶓi,t = Error term
However, we need to transformed the model into logarithm form based on a reason illustrated
by the researcher. According to Gevorkyan and Kvangraven(2016), the purpose of
implementing logarithm is to prevent scale effects. In this case, the scalability of leverage is
strongly depended on the independent variables such as tangibility, size, growth, and
profitability. For example, even though firms with higher tangible assets are allow to continue
apply borrowings, leverage suffered by them will continue increase. Creditors and lenders may
worry firms unable to repay their borrowings due to their higher leverage. Therefore, this will
create a scale effect which is firms will increase interest rate payments on borrowings to avoid
late borrowings repayments. On the other hand, sometimes, creditors and lenders may not
decide to extend credit repayments no matter how much the growth, size, and profits of the
firms. The reason is creditors and lenders worried that firms will fail to settle their borrowings
repayments if they continue default their borrowings repayments. In this case, scale effects are
lenders and creditors that unwilling to provide extension of credit repayments. Since there is
no extension given in credit repayments, companies are force to settle their borrowings
repayment according to deadline provided by creditors and lenders. So, in order to prevent
these scale effects which is higher cost of borrowings and no credit repayment extension
influence firms leverage, we need to transformed the model into logarithm form. After we
transformed the model into logarithm formed, we expressed the model as:
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CHAPTER 3: METHODOLOGY
lnLEVi,t = α0 + α1 lnTANGi,t +α2 lnSIi,t +α3lnGRi,t +α4 lnPROi,t +ᶓi,t
(2)
Where: ln LEV = leverage in Malaysia in logarithm form
In TANG = Tangibility in Malaysia in logarithm form
ln SI = Size in Malaysia in logarithm form
ln GR = growth in Malaysia in logarithm form
ln PRO = profitability in Malaysia in logarithm form
i,t =
Cross section Annual Time Period from 1999 to 2021
α0 = Constant
ᶓi,t = Error term
3.3 Data, Measurement, Sources, Expected Sign and Descriptive Statistic
3.3.1 Data, Measurement and Sources
Secondary data had used to examine the relationship between leverage and the other
independent variables. First, according to Shah and Hijazi (2004), leverage is measured by total
assets ratio divided by total debt. According to Shah and Hijazi (2004), total assets ratio is
computed by using company earnings after taxes divided by total assets. Company earnings
after taxes and total assets are measured in RM million. Total debt referred as the combination
of company current liabilities and non -current liabilities and it is measured in RM million.
This can be
simplified
Data about company earnings after taxes, total assets, and total debt from 1999 to 2021 are
taken from companies’ annual report that include Kossan Rubber Industries Berhad, Tan
Chong Motor Holdings Berhad and Mah Sing Group Berhad. Annual report from these 3
companies is extracted from Bursa Malaysia website. Second, according to Shah and Hijazi
(2004), tangibility is measured by total gross fixed assets divided by total assets. The unit of
measurements for total gross fixed assets and total assets is measured in RM million. In order
to compute tangibility, data on total gross fixed assets and total assets had extracted from
Kossan Rubber Industries Berhad, Tan Chong Motor Holdings Berhad and Mah Sing Group
Berhad annual reports from 1999 to 2021. Third, Shah and Hijazi (2004) opined that size is
calculated through transformed sales value into logarithm form. This is done by gathering sales
data from 1999 to 2021 from Kossan Rubber Industries Berhad, Tan Chong Motor Holdings
Berhad and Mah Sing Group Berhad annual reports. Companies’ sales are measured in RM
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CHAPTER 3: METHODOLOGY
million. Next, according to Shah and Hijazi (2004), growth of companies can compute through
taking the changes in company total assets annually. Changes in total assets from Kossan
Rubber Industries Berhad, Tan Chong Motor Holdings Berhad and Mah Sing Group Berhad
can observe through their balance sheet from 1999 to 2021. Changes in total assets is measured
in RM million. Lastly, profitability can measure through using net income before taxes divided
by total assets. Net income before taxes and total assets are measured in RM million. Data
about net income before taxes and total assets are taken from Kossan Rubber Industries Berhad,
Tan Chong Group Berhad and Mah Sing Group Berhad annual reports from 1999 to 2021.
3.3.2 Expected Sign
According to Shah and Hijazi (2004), leverage is an important indicator to determine
corporate borrowings in Malaysia. According to Shah and Hijazi (2004), leverage can cause
firms to suffer bankruptcy risk. Firms that suffered higher leverage may confront larger risk of
going bankrupt because they have a lot of unpaid borrowings repayments. On the other hand,
firms with lower leverage will have a low chance of getting bankrupt. Since their chance of
getting bankruptcy is very slim, therefore, they have higher capability to repay their corporate
borrowings. According to Shah and Hijazi (2004), leverage is also influenced by borrowings
repayments which may be due during short run and long run. If firms are given a short period
of time to clear their borrowings repayments, firms may not have enough time to figure out
how to clear away their debts. In that case, firms may borrow from the other parties to settle
their previous debt. Since firms borrow from the other parties to repay their borrowings, firms
may eventually suffer a larger leverage. On the other hand, if firms are given a longer debt
repayment period, firms have ample time to decide on how they are going to repay their debt.
In that case, firms will not choose to demand borrowings for repaying their debt immediately.
Thus, this will force demand for corporate borrowings to fall. Leverage should serve as a proxy
for corporate borrowings since it is able to determine corporate borrowings. In this study, I
expected the sign for leverage to be positive. The first reason is leverage can influence
corporate borrowings repayments. For example, people that suffered a larger leverage have
higher possibility of not being able to repay their borrowings. The second reason is borrowing
repayments periods. For example, a longer borrowings repayments period will not force firms
to demand borrowings immediately from the other parties for repaying their debt since they are
provided with ample time to decide on which alternative can be used to repay their debts.
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CHAPTER 3: METHODOLOGY
3.3.2.1 Tangibility
According to Rus and Samiran (2012), tangibility refers to the amount of tangible or fixed
assets held by firms. Examples of tangible assets are computers, fixtures and fittings in the
office and machines. Tangible assets have a larger liquid value; therefore, firms can use them
as collateral when applying borrowings. According to Rus and Samiran (2012), firms that own
large amounts of tangible assets can apply for borrowings with a lower interest rate. In that
case, firms will choose to apply more borrowings since they bear lower interest rate payments
from corporate borrowings. This will force firms to bear a larger leverage since their demand
on corporate borrowings increases. On the other hand, firms with lesser tangible assets will
bear higher interest rates when demand for corporate borrowings. In that case, firms may suffer
a higher leverage since they need to pay a higher interest rate to attain corporate borrowings.
In this case, we employed tangibility as the proxy to influence leverage and expected a
positive sign for tangibility. The reason is we found out that there is a direct relationship
between tangibility and leverage and this direct relationship between tangibility and leverage
is consistent with trade off theory. According to trade off theory, as tangibility increase,
leverage also increases vice versa.
3.3.2.2 Size
According to Chow (2019), size represents the business operation of the company.
According to Chow (2019), big companies faced larger leverage compared to small companies.
Big companies will encounter lower chances to get bankruptcy because they are more
diversified. Since big companies have fewer bankruptcy risks, they can apply for borrowings
and this would increase their leverage. Second, big companies are known by various market
participants because they have operated in the market for many years (Chow, 2019). So, big
companies may successfully build good rapport with many market participants. With a good
rapport with market participants, this allows big companies to demand borrowings with lesser
interest rate payments (Chow, 2019). Since big companies are required to pay fewer interest
rate when demand borrowings, big companies can apply more borrowings and lastly will
increase their leverage (Chow, 2019).
On the other side, small firms will face higher bankruptcy risks compared to big firms
because they are not diversified. Apart from that, small firms may not have good relationships
with market participants because they are not known very well by the participants in the market
(Chow, 2019). Since small firms have poor relationships with market participants, they may
not be able to negotiate for better terms of borrowings (Chow, 2019). For example, small firms
49
CHAPTER 3: METHODOLOGY
may not be able to apply borrowings with lower interest rate payments and longer periods for
repaying borrowings. Therefore, small firms should reduce borrowing activities to prevent
themselves from suffering higher leverage. The lower the leverage suffer by small firms, the
less the chance they will get bankruptcy.
In this case, size is the proxy that determines leverage. In this study, I expect a positive
sign on size because there is a positive relationship between size and leverage.
3.3.2.3 Growth
According to Shah and Hijazi (2004), growth of a company can be observed through the
changes in total assets. Total assets referred to the combination of company current assets and
noncurrent assets.
An increase in firms’ total assets indicated that firms are growing. According to Shah and
Hijazi (2004), growing firms will face a higher leverage. Firstly, growing firms need
borrowings to build up their business. For example, growing firms used borrowings to purchase
machinery for boosting firm productivity. Second, growing firms need to use borrowings to
support their investment activities (Chow, 2019). An example of investment activity is building
a new plant for producing goods, since growing firms need to repay borrowings in future, in
that case, leverage bear by growing firms currently will become higher.
A drop in firms total assets indicated that firms are not growing. According to Shah and
Hijazi (2004), non-growing firms will face lower leverage. Non-growing firms are not
advisable to demand more borrowings. Non-growing firms may not be able to sell their fixed
assets for repaying their borrowings debts because fixed assets owned by them is lesser (Chow,
2019). If non-growing firms sell their assets to repay their borrowing debt, non-growing firms
may not have enough assets to produce goods and services (Chow, 2019). Therefore, nongrowing firms should not involve in borrowing activity because it is too risky for them
especially when they fail to settle borrowings repayments.
In this case, growth is assigned as a proxy for leverage. In this study, I would expect the
variable growth should be a positive sign because the relationship between growth and leverage
is positively related.
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CHAPTER 3: METHODOLOGY
3.3.2.4 Profitability
According to Chow (2019), profitability is defined as earnings gained by the company in
the sector. The public can attain information about company profits through browsing their
company annual reports. According to Chow (2019), changes that happened in company
profitability will determine their leverage.
According to Chow (2019), high profits companies will face less financial burden. Since
high profits firms have fewer financial problems, they are allowed to demand borrowings
(Chow, 2019). High profits firms can demand borrowings because they are able to repay the
borrowings (Chow, 2019). High profits firms will suffer higher leverage if they demand for
borrowings (Chow, 2019).
On the other hand, low profit companies are more likely to encounter larger financial
problems. So, low profits firms are discouraged from demanding borrowings (Chow, 2019).
Low profits firms may suffer a lower leverage if they demand less borrowings (Chow, 2019).
So, profitability should be employed as a proxy that influences leverage. In this study, I
proposed a positive sign for profitability because studies revealed that there is a positive
relationship established between profitability and leverage.
3.3.3 Descriptive Statistics
Descriptive statistics can be defined as the use of numbers and graph approach to analyse
and present data (Fisher & Marshall, 2009). Type of descriptive statistics that used to illustrate
the sample size relies on the measurement level that was used (Fisher & Marshall, 2009). The
3 levels of measurement are known as nominal, ordinal and continuous (Fisher & Marshall,
2009).
In descriptive statistics, mean is used to describe central tendency of population or sample.
If we randomly select a mean sample from the population, the sample mean will become
unbiased. It means that if we repeatedly draw random sample mean from the population, our
sample mean on average will be the same as the population mean. According to Miller (2013),
sample mean that obtained will not equal the population mean. The reason is we cannot identify
the exact value for population mean, therefore, we fail to calculate the difference value between
sample mean and population mean. Even though the difference value between sample mean
and population mean cannot be found out, we can use random sampling to estimate the distance
between sample mean and population mean. The reason is because the shape of the sample
mean distribution is a normal distribution.
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CHAPTER 3: METHODOLOGY
According to Vicente and Ruiz (2009), variance is defined as the data that dispersed
around the mean. Data dispersion strongly depends on variance (Vicente & Ruiz, 2009). An
increase in variance will cause data that dispersed around the mean was higher (Vicente &
Ruiz, 2009). On the other hand, if variance is smaller, data that dispersed around the mean is
also lower. However, data may vary if there is a change in variance. According to Vicente and
Ruiz (2009), standard deviation needs to be employed to capture variability of data. Standard
deviation can measure data variability because it will square root the value computed from
variance (Vicente & Ruiz, 2009). So, when the value of variance changes, standard deviation
also will change.
Skewness can indicate the distribution of data around the mean (Vicente & Ruiz, 2009).
A zero-skewness showed that the sample is distributed equally around the mean (Vicente &
Ruiz, 2009). If the sample is distributed equally around the mean, this forms a symmetric
distribution. A symmetric distribution will form a bell-shaped curve (Vicente & Ruiz, 2009).
If the skewness is negative, it means the distribution of data is left-skewed (Vicente & Ruiz,
2009). In this case, data distribution only focuses on the right side of the mean. If the skewness
is positive, this indicates data only distributed on the left side of the mean (Vicente & Ruiz,
2009). Since the data only focus on the left side of the mean, therefore, this skewness is known
as right-skewed (Vicente & Ruiz, 2009).
Kurtosis represents the general height of a distribution curve over range of score (Miller,
2013). Flat distribution curve is known as “platykurtik”(Miller, 2013). Peaked distribution
curve is known as “leptokurtik”(Miller, 2013). A normal distribution curve is known as
“mesokurtik”(Miller, 2013).Mode refers to the figure that appears the most in an
observation(Vicente & Ruiz, 2009). Median is defined as the middle number in an observation
(Vicente & Ruiz, 2009).
The Jarque Bera test is a test that determines whether the model is a normal distribution
or not. This is done through taking sample data to calculate kurtosis and skewness. If the
kurtosis and skewness of sample data follow the normal distribution, therefore we can say the
model is normally distributed. Probability defined as the possibility or chance of an event will
occur. The probability of an uncommon event happening is very slim because it is
approximately 0. On the other hand, the probability of a common event occurring is nearly 1.
Therefore, we can say that the data is facing time varying mean of time varying covariance
because the data will change across time (Trochim 2020).
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CHAPTER 3: METHODOLOGY
3.4 Econometric Model
3.4.1 Panel Regression
Panel regression is known as balance panel regression (Zulfikar, n.d.). It is the mixture
of cross-sectional data and time series data (Zulfikar, n.d.).
In this study, the researcher intends to study determinants of corporate borrowings in
Malaysia from 1999 to 2021. The researcher had selected 3 companies to study determinants
of corporate borrowings in Malaysia. These 3 companies are Kossan Rubber Industries Berhad,
Tan Chong Motor Holdings Berhad and Mah Sing Group Berhad. In this case, the number of
cross- section refers to the number of companies chosen to study determinants of corporate
borrowings in Malaysia. Total panel observations refer to the number of observations involved
in this study. In this case, the number of years is 23 years because the study period is from 1999
to 2021. Number of chosen companies in this study is 3. Therefore, total panel observation is
23 × 3 = 69.
3.4.1.1 Pooled Ordinary Least Square (OLS)
Pooled ordinary least squares (OLS) is used to estimate the variables from balance panel
data (Zulfikar, n.d.). Pooled Ordinary Least Square (OLS) is also known as the constant
coefficient model (Zulfikar, n.d.).
The advantage of pooled ordinary least squares (OLS) is it can cause higher degrees of
freedom because the combination of cross sectional and time series data has increased the
number of data (Zulfikar, n.d.). Next, balance panel data is more appropriate to conduct
dynamic adjustment because it has repeated cross sectional observations (Zulfikar, n.d.).
The objective of employing panel regression is to test how the independent variables
influence leverage in Malaysia.
The panel regression which be as followed:
lnLEVi,t = α0 + α1 lnTANGi,t +α2 lnSIi,t +α3lnGRi,t +α4 lnPROi,t +ᶓi,t
Where: ln LEV = leverage in Malaysia in logarithm form
In TANG = Tangibility in Malaysia in logarithm form
ln SI = Size in Malaysia in logarithm form
ln GR = growth in Malaysia in logarithm form
ln PRO = profitability in Malaysia in logarithm form
i,t =
Cross section Annual Time Period from 1999 to 2021
α0 = Constant
ᶓi,t = Error term
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CHAPTER 3: METHODOLOGY
From Pooled OLS, we can determine the relationship between independent variables that
influence leverage in Malaysia by using p-test. The hypothesis test that formed to analyse how
independent variables determine leverage will show below:
Firstly, to test the direct relationship between tangibility and leverage, hypothesis test will be:
H0: α1 = 0 (Tangibility in logarithm cannot influence leverage)
If testing for the significance of variable, it should be
H1: α1 > 0 (Tangibility in logarithm can influence leverage positively)
From hypothesis test above, we will reject the null hypothesis if the p-value for ln TANG
is smaller than the significance level. After we rejected the null hypothesis, this indicated that
tangibility can influence leverage positively. On the other hand, if p-value for ln TANG is
larger than the significance level, this shows that we fail to reject the null hypothesis. In this
case, if we fail to reject the null hypothesis, this indicated tangibility is unable to determine
leverage.
Next, to test the direct relationship between size and leverage, the null hypothesis will be:
H0: α2 = 0 (Size in logarithm cannot influence leverage)
H1: α2 > 0 (Size in logarithm can influence leverage positively)
From hypothesis test above, we will reject the null hypothesis if we find that p-value for
ln SI is lower than the significance level. In this case, when we reject the null hypothesis, this
indicates that size can determine leverage directly. On the contrary, if the p-value for ln SI is
larger than the significance level, we fail to reject the null hypothesis. If we fail to reject the
null hypothesis, this indicates ln SI has no relationship with leverage.
Third, to test the positive relationship between growth and leverage, hypothesis test will be:
H0: α3= 0 (Growth in logarithm cannot influence leverage)
H1: α3> 0 (Growth in logarithm can influence leverage positively)
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CHAPTER 3: METHODOLOGY
From hypothesis test above, if p-value for ln GR is smaller than the significance level,
we reject the null hypothesis. If we reject the null hypothesis, this indicates size can determine
leverage directly. On the other hand, if the p-value for ln GR is larger than the significance
level, we fail to reject the null hypothesis. If we fail to reject the null hypothesis, this shows
that growth is unable to determine leverage.
Lastly, to test positive relationship between profitability and leverage, hypothesis test will form
as below:
H0: α4 = 0 (Profitability in logarithm cannot influence leverage)
H1: α4 > 0 (Profitability in logarithm can influence leverage positively)
From hypothesis test above, if p-value for ln PRO is lower than the significance level,
we will reject the null hypothesis. In this case, if we reject the null hypothesis, it shows that
profitability is able to influence leverage directly. On the other side, if p-value for ln PRO is
larger than the significance level, we may fail to reject the null hypothesis. If we fail to reject
the null hypothesis, it indicates that profitability is unable to determine leverage.
3.4.1.2 Fixed Effects
Fixed effects stated that differences that happen in cross-section are caused by differences
in intercept (Zulfikar, n.d.). To eradicate intercept differences that happen in fixed effect
models, the researcher can adopt dummy variables (Zulfikar, n.d.). Difference in intercept is
caused by certain reasons such as work culture and management differences (Zulfikar, n.d.).
In the study on how leverage is influenced by the independent variables, the researcher
needs to adopt dummy variables to capture the fixed effects in the panel ordinary least square
regression (OLS). After the researcher insert dummy variable in the fixed effect model, the
model will become as follow:
lnLEVi,t = α0 +α1 lnTANGi,t +α2 lnSIi,t +α3lnGRi,t +α4 lnPROi,t + α₅ D1 +ᶓi,t
Where: ln LEV = leverage in Malaysia in logarithm form
In TANG = Tangibility in Malaysia in logarithm form
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(3)
CHAPTER 3: METHODOLOGY
ln SI = Size in Malaysia in logarithm form
ln GR = growth in Malaysia in logarithm form
ln PRO = profitability in Malaysia in logarithm form
D1 = 1 if young firm
0 if not young firm
i,t =
Cross section Annual Time Period from 1999 to 2021
Since the number of cross section companies is 69, after we included the dummy variable in
this regression, the total of cross section became 68 because we employed a formula which is
m-1. This formula, m- 1 is to prevent dummy traps or multicollinearity from happening in this
model.
To determine the fixed effect from model (3), the hypothesis test must employ as below:
H0: α1 = α2 = α3 = α4 =α5 = 0 (Pooled Model is better)
H1: At least one αi ≠ 0 (Fixed Model is better)
From hypothesis test above, we employed p-test to determine our decision rules whether
reject or fail to reject the null hypothesis. If p-value is larger than significance level, we reject
the null hypothesis and this implies that we should convert our pooled OLS regression into a
fixed effects model because a fixed effects model is better than pooled OLS regression model.
If p-value is larger than significance level, we fail to reject the null hypothesis. If we are unable
to reject the null hypothesis, we should not convert our model into fixed effects because a
pooled model is better than fixed effect model.
3.4.1.3 Random Effect
Random Effect is known as Error Component Model (ECM) or Generalised Least Square
(GLS) method (Zulfikar, n.d.). In a random effects model, variables relate to each other across
time and person (Zulfikar, n.d.). In this model, ordinary least square (OLS) is not applicable;
rather it is using maximum likelihood or general least square (Zulfikar, n.d.). The objective of
the random effects model is to eradicate heteroscedasticity (Zulfikar, n.d.)
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CHAPTER 3: METHODOLOGY
In the random effects model, intercept is considered a random variable because intercept
differs among individuals (Zulfikar, n.d.). Therefore, we need to form 2 residuals since
intercept is a random variable (Zulfikar, n.d.). The first residuals that formed got the mixture
of time series data and cross-sectional data (Zulfikar, n.d.). The second residual that formed
represents an individual residual and it is random because of the i-th unit (Zulfikar, n.d.).
In the study on determinants of corporate borrowings in Malaysia, the random effect
model will estimate as below:
lnLEVi,t = α0 + α1 lnTANGi,t +α2 lnSIi,t +α3lnGRi,t +α4 lnPROi,t + Vi +ᶓi,t (4)
Where: N = number of companies or cross section
T = number of time period
ln LEV = leverage in Malaysia in logarithm form
In TANG = Tangibility in Malaysia in logarithm form
ln SI = Size in Malaysia in logarithm form
ln GR = growth in Malaysia in logarithm form
ln PRO = profitability in Malaysia in logarithm form
Vi = individual residual where random characteristics of unit observation
i-thand remains at all times
ᶓi,t = Residual as a whole and the residual is combination of cross section and
time series
Referring to the equation above, the intercept α0 is considered a random variable.When both
residuals which is Vi and ᶓi,t from equation above combine together, it will become composite
error term represented by wit.
3.4.2 Diagnostic checking
Diagnostic checking can be defined as a type of test that is conducted to identify models
before we do forecast. The purpose is to ensure our model follows the time series data in our
research. Therefore, test such as Jarque Bera (JB) test, Heteroscedasticity test, serial correlation
LM test, Cumulative Sum (CUSUM) Test, Autoregressive Conditional Heteroscedasticity
(ARCH), CUSUM of Squares Test need to be carry out.
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CHAPTER 3: METHODOLOGY
3.4.2.1 Jarque- Bera (JB) Test
The purpose of conducting the JB test is to check whether the model is normally
distributed or not. This is done through calculating kurtosis and skewness by using the number
of observations provided in the model or equation. If we applied a JB test and found out that
our model is not a normal distribution, this implies that results obtained from the model are
unable to be trusted because the result was biased. Therefore, JB equation can express as below:
JB =
𝑁
6
(S2 +
(𝐾−3)²
4
)
(5)
where N = sample size
S = Skewness of sample
K = Kurtosis of sample
The hypothesis test for JB test is as follow:
H0: Model is normally distributed
H1: Model is not normally distributed
From hypothesis test above, if our p-value is smaller than the significance level, we will
reject the null hypothesis. If we reject our null hypothesis, we found out that our model is not
normally distributed. This indicated that we may have possibility getting a skewed model if
our model is not normal distributed. Our model also may become skewed if the kurtosis and
skewness obtained from normal distribution is unequal with the predicted value. On the other
side, if p-value is larger than significance level, we fail to reject the null hypothesis. If we fail
to reject the null hypothesis, we will get a normal distribution model.
3.4.2.2 Serial Correlation LM test
check if panel is using BG test for autocorrelation checking
Serial Correlation LM test is also known as Breusch – Godfrey test. This test can use to
detect autocorrelation problem. Autocorrelation happen because residual is depended on one
another. Even though we found the model is linear, unbiased, and asymptotically normal under
auto serial correlation, the existing of autocorrelation will cause the model does not produce
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CHAPTER 3: METHODOLOGY
the smallest variance anymore. When variance from the model is not at the smallest, the model
is said less effective when compared to another model. Since variance from the model is not
the smallest, the model does not fulfil Best Linear Unbiased Estimator (BLUE).
The objective of implementing LM test is to eradicate autocorrelation problem that
happen in the model. If the error term from the model is correlated with each other and does
not have zero covariance, therefore, we can infer that the model is facing autocorrelation
problem. According to the 7 assumptions from Classical Normal Linear Regression Model
(CNLRM), the assumption 5 stated that the error term should not correlated with each other.
The reason is if error term correlated with each other, the model is likely to encounter
autocorrelation issue. If error term from the model is correlated with each other, the model is
violated against assumption 5 from CNLRM. If the model violates against CNLRM, the
consequence is the test such as chi-squares test, F-test and T-test will give an inaccurate result.
The LM test equation will be expressed as below:
Yt =α1 +α2Xt+μt
where μt
(6)
= ρ1 μt-1 + ρ2 μt-2 + … + ρρμt-ρ + εt
The hypothesis test for LM test will follow as below:
H0: No autocorrelation exists in this model
H1: Autocorrelation exist in this model
From the hypothesis test above, if the LM test statistics is greater than chi-square critical
value, we will reject the null hypothesis. If we reject the null hypothesis, this indicates that the
model has autocorrelation problems. If LM statistics is lower than chi-square critical value, we
fail to reject the null hypothesis. If we fail to reject the null hypothesis, this showed that the
model does not have autocorrelation problem.
3.4.2.3 Heteroscedasticity test
Heteroscedasticity means the variance of error term is not consistent. Therefore, we need
to conduct heteroscedasticity test to determine whether the independent variables can influence
error term. According to 7 assumptions of Classical Normal Linear Regression Model
(CNLRM), assumption 4 stated that the model should have homoscedasticity (Gujarati
&Porter, 2009). Homoscedasticity will make variance of the model become constant; therefore,
our model no longer faces heteroscedasticity issue (Gujarati &Porter, 2009). If the estimated
variance is not constant, it is violated against assumption 4 of CNLRM because our model is
facing heteroscedasticity issue (Gujarati &Porter, 2009). Consequently, we cannot use t- test,
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CHAPTER 3: METHODOLOGY
F-test and confidence interval to test our model because it will produce wrong results if we use
them.
To conduct heteroscedasticity test, the equation will form as below:
Yi = α0 + α1 X1i +α2 X2i + μi
(7)
Where μ2i = α0 + α1 X1i +α2 X21i +α4X22i + α5 X1i X2i + vi
The hypothesis test for Heteroscedasticity test will be:
H0: No heteroscedasticity in this model
H1: Heteroscedasticity happen in this model
The formula of chi-square statistics is n × R2. From the hypothesis test above, if chisquare statistics is larger than chi-square critical, we will reject the null hypothesis. After we
reject the null hypothesis, we found that the model encounter heteroscedasticity problem. If
chi-square statistics is lower than chi-square, we fail to reject the null hypothesis. If we fail to
reject the null hypothesis, this indicates the model does not face any heteroscedasticity
problem.
3.4.2.4 Cumulative Sum (CUSUM) Test
The objective of CUSUM test is to combat stability issues faced by coefficient in the
model. Researcher will use CUSUM test to detect structural flaw that happen in the model.
Therefore, the CUSUM equation will express as below:
Wt = ∑𝑡𝑟=𝑘+1
𝑊𝑟
(8)
𝜎
Where wr =recursive residual
𝜎 = standard deviation of recursive residual
t = k + 1, ……, T
The hypothesis test for CUSUM test is:
H0: Data in this model is random
H1: Data in this model is non-random
From hypothesis test above, we fail to reject the null hypothesis if CUSUM test statistic
is higher than critical value. When we fail to reject the null hypothesis, we found that data
incorporated in our model is random. Besides that, CUSUM diagram can be implemented to
determine rejection region. In the CUSUM diagram, there is one CUSUM line followed by
another 2 crucial lines will be shown. If the CUSUM line is in between these 2 crucial lines,
this showed that we fail to reject the null hypothesis. So, when we fail to reject the null
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CHAPTER 3: METHODOLOGY
hypothesis, we also found out that data incorporated in our model is random. If the data in our
model is random, this cause data and parameter in our model become stable.
According to CUSUM diagram, if our CUSUM line cross the critical line boundary,
we will reject the null hypothesis. After we rejecting the null hypothesis, we found out data
incorporated in our model become non -random. Since the data incorporated in the model is
non-random, parameter and data in the model will become instable.
3.4.2.5 CUSUM of Squares (CUSUMSQ) Test
CUSUMSQ test have the same decision rules and hypothesis test as CUSUM test. The
objective of using CUSUMSQ test is to prevent coefficients in our model from having
instability. According to Brown, Durbin, and Evans (1975) viewpoint, there is a difference
CUSUM test and CUSUMSQ test. The reason is recursive residual from CUSUM test is
different from CUSUMSQ test. Recursive residual used in CUSUM test known as cumulative
recursive residual whereas recursive residual used in CUSUMSQ test is known as squares
recursive residual. Therefore, CUSUMSQ test is better than CUSUM test because results
attained by using CUSUMSQ test is more reliable compared to CUSUM test.
The equation for CUSUMSQ test can expressed as below:
E(St) =
(𝑡−𝑘)
(9)
(𝑇−𝐾)
3.5 Hypothesis of study
According to literature review from chapter 2, many research had conducted by various
researchers to study the determinants of corporate borrowings in Malaysia. There is different
point of view contributed by researchers to illustrate how the independent variables such as
tangibility, size, growth, and profitability able to influence leverage which is the dependent
variable in our study. Studies found out that there is contradiction thought among researchers
to illustrate how independent variables able to determine leverage. Some researchers argue that
the independent variables such as tangibility, size, growth, and profitability can determine
leverage positively. On the other hand, some researchers argue that variables such as
tangibility, size, growth, and profitability can determine leverage negatively. The result gain
by different researchers is not consistent because each researchers selected different industry
and different sample size to conduct this research.
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CHAPTER 3: METHODOLOGY
Since there is a contradiction outcome among researchers, therefore, it is very crucial to
conduct research that study on determinants of corporate borrowings in Malaysia. The purpose
of conducting this research is to see how leverage is influence by the independent variables.
Therefore, variables such as tangibility, profitability, size, and growth will be incorporated in
this research. The study hypothesis will show below:
significant
i)There is a positive relationship between tangibility and leverage in Malaysia.
ii) There is a positive relationship between size and leverage in Malaysia.
iii) There is a positive relationship between growth and leverage in Malaysia.
iv) There is a positive relationship between profitability and leverage in Malaysia.
62
References
Adam Aziz and Syafiqah Salim/The Edge MalaysiaAugust 04, 2022 14:00 pm +08. (2022,
August 4). Cover story: Debt crunch unlikely. The Edge Markets. Retrieved May 4, 2023, from
https://www.theedgemarkets.com/article/cover-story-debt-crunch-unlikely
About credit statistics. The Bank for International Settlements. (2017, September 17).
Retrieved May 4, 2023, from https://www.bis.org/statistics/about_credit_stats.htm?m=2673
Azhari, N. K. M., Mahmud, R., & Shaharuddin, S. N. H. (2022). HOW DOES INTERNAL
CAPABILITY INFLUENCE COMPANIES’FINANCIAL PERFORMANCE DURING
COVID-19?. International Journal of Accounting, 7(43), 489-502.
Alkhatib, K. (2012). The determinants of leverage of listed companies. International journal
of business and social science, 3(24).
Chow, Y. P. (2019). Sectoral Analysis of the Determinants of Corporate Capital Structure in
Malaysia. Organizations and Markets in Emerging Economies, 10(20), 278-293.
Cortez, M. A., & Susanto, S. (2012). The determinants of corporate capital structure: Evidence
from Japanese manufacturing companies. Journal of International Business Research, 11(3),
121.
Choi, J. K., Yoo, S. K., Kim, J. H., & Kim, J. J. (2014). Capital structure determinants among
construction companies in South Korea: A quantile regression approach. Journal of Asian
Architecture and Building Engineering, 13(1), 93-100.
Do, T. V. T. (2021). DETERMINANTS OF CORPORATE DEBT MATuRITY: EVIDENCE
FROM THE CONSuMER GOODS SECTOR IN VIETNAM. Innovations, 18(3), 175-182.
Fisher, M. J., & Marshall, A. P. (2009). Understanding descriptive statistics. Australian critical
care, 22(2), 93-97.
Gevorkyan, A. V., & Kvangraven, I. H. (2016). Assessing recent determinants of borrowing
costs in sub‐Saharan Africa. Review of Development Economics, 20(4), 721-738.
Gujarati, D. N. (2022). Basic econometrics. Prentice Hall.
63
Goh, C. F., Tai, W. Y., Rasli, A., Tan, O. K., & Zakuan, N. (2018). The determinants of capital
structure: evidence from Malaysian companies. International Journal of Supply Chain
Management, 7(3), 225-230.
Ibrahim, H., & Lau, T. C. (2019). The determinants of financial leverage for surviving listed
companies in Malaysia. International Journal of Business and Society, 20(1), 75-94.
Jaafar, M. N., Muhamat, A. A., Ahmad, I., & Syed Alwi, S. F. (2017). Determinants of
capital structure: Empirical evidence from Shariah compliant plantation firms in Malaysia.
Journal of Emerging Economies & Islamic Research, 5(4), 1-9.
Jose Barrock/The Edge MalaysiaFebruary 20, 2023 15:00 pm +08. (2023, February 20).
MOGSC seeks government help in recovering debts from Sapura Energy. The Edge Markets.
Retrieved May 4, 2023, from https://www.theedgemarkets.com/node/654990
Lending interest rate (%). World Bank Open Data. (n.d.). Retrieved May 4, 2023, from
https://data.worldbank.org/indicator/FR.INR.LEND
Pérez-Vicente, S., & Ruiz, M. E. (2009). Descriptive statistics. Allergologia
etimmunopathologia, 37(6), 314-320.
Morri, G., & Cristanziani, F. (2009). What determines the capital structure of real estate
companies? An analysis of the EPRA/NAREIT Europe Index. Journal of Property Investment
& Finance.
Malay Mail. (2023, January 3). Troubled China Evergrande pledges to repay debts in 2023.
Malay
Mail.
Retrieved
May
4,
2023,
from
https://www.malaymail.com/news/money/2023/01/03/troubled-china-evergrande-pledges-torepay-debts-in-2023/48234
Miller, W. (2012). Descriptive statistics. In Statistics and Measurement Concepts with
OpenStat (pp. 19-51). New York, NY: Springer New York.
64
Marimuthu, M., & Hamzah, H. H. (2020). Determinants of long-term financing decisions: an
empirical investigation on the oil and gas firms in Malaysia. Platform: A Journal of
Management and Humanities, 3(1), 59-70.
M’ng, J. C. P., Rahman, M., & Sannacy, S. (2017). The determinants of capital structure:
Evidence from public listed companies in Malaysia, Singapore and Thailand. Cogent
Economics & Finance, 5(1), 1418609.
Mustapha, M., Ismail, H., & Minai, B. (2011). Determinants of debt structure: Empirical
evidence from Malaysia. In 2nd International Conference on Business and Economic Research
2nd ICBER proceeding.
Nejad, N. R., & Wasiuzzaman, S. (2013). The empirical analysis of capital structure
determinants–Evidence from Malaysia. Engineering and Technology, 74, 466-474.
Rus, M. (2012). The determinants of capital structure for Malaysian construction firms.
Md-Yusuf, M., Yunus, F. M., & Supaat, N. Z. L. M. (2013). Determinants of capital structure
in Malaysia electrical and electronic sector. International Journal of Economics and
Management Engineering, 7(6), 1514-1519.
Shah, A., Hijazi, T., & Javed, A. Y. (2004). The determinants of capital structure of stock
exchange-listed non-financial firms in Pakistan [with comments]. The Pakistan Development
Review, 605-618.
Saarani, A. N., & Shahadan, F. (2013). The determinant of capital structure of SMEs in
Malaysia: evidence from enterprise 50 (E50) SMEs. Asian Social Science, 9(6), 64.
Seng, C. K., & Thaker, H. M. T. (2018). Determinants of corporate hedging practices:
Malaysian evidence. Report on Economics and Finance, 4(4), 199-220.
Tarazi, R. E. (2013, May). Determinants of capital structure: evidence from Thailand panel
data. In Proceedings of 3rd Global Accounting, Finance and Economics Conference (No. 1958,
pp. 1-9).
65
Vătavu, S. (2013). Determinants of corporate debt ratios: Evidence from manufacturing
companies listed on the Bucharest Stock Exchange. Timisoara Journal of Economics and
Business, 6(20), 99-126.
Zulfikar, R., & STp, M. M. (2018). Estimation model and selection method of panel data
regression: an overview of common effect, fixed effect, and random effect model. INA-Rxiv.
doi: https://doi. org/10.31227/osf. io/9qe2b.
66
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