1         CHAPTER 1 INTRODUCTION

 

1.1        Introduction of Chapter

The aim of this study is to analyse the impact of determinants of capital structure. This chapter of the study will make proper and extensive to the background of the study. The statement of the problem shall also be discussed in this chapter alongside the research objectives and research questions. The formylation of hypothesis and the significance of the study shall be discussed, and the chapter will further present the chart of the research process along the definition of terms. The chapter will be rounded up with the chapter summary.

1.2        Background of Study

Capital structure according to Fisseha (2010) is the ratio of a firm’s debt value ratio to its own equity and liabilities. It is a concept that offers insight into how firm combines different sources of funds to finance its operations in order to maximize the value of a firm. In most cases, these sources range from equity capital to liabilities to debts, etc.

Capital structure is one of the business phenomenal that has attracted a lot of concern from firms and managers across the globe. This is because a firm’s financial performance significantly depends on its capital structure.  Hedau, et al. (2018). It is very crucial due to its dependence on the firm’s form financing and type of funding source. Thus, managers are concerned about how firm’s financing process will ensure continuity of the firm’s growth and competitiveness, and how the funding process will guarantee adequate source, utilization and return of funds to their sources (Šarlija & Harc, 2012). By doing this, revenue generated from debt will be greater than the cost of acquiring capital.

Until the duo of Modigliani and Miller proposed in 1958 a modern theory of capital structure in their paper titled “the cost of capital, corporate finance and the theory of investment”, there have been no controversies surrounding the nexus between capital structured and value of firm.

In their paper, the duo opined that if some specific assumptions could hold, then capital structure will be irrelevant to determine the firm’s value and cost of capital. The main assumptions proposed by the duo are lack of transaction cost, lack of bankruptcy costs, absence of taxes and perfect financial market in the economy. Although, the duo further presented an upgraded version of the model in the same year by integrating taxes and concluded that debt is a source of finance that increases the company’s value and decreases linearly weighted average cost of capital (Silva, Cerqueira, & Brandão, 2017). Their assumption of perfect financial market coupled with lack of taxation and transaction costs was to be unrealistic and attracted scholars to propose various theories.

By adopting the trade-off theory, Kraus and Litzenberger (1973) and Graham and Harvey, (2001) repudiated Miller and Modigliani’s assumption of no “transaction cost and no taxation cost”. They suggest that firm considers costs and benefits that are associated with the usage of debt and equity before choosing the optimal capital structure. They however noted that the trade-off theory presupposes a target debt ratio. This ratio is used to explain the trade-off that exist between cost associated with the financial distress and the cost of borrowing/debts.

They concluded by positing that a firm’s market value is maximized at a particular level and this level is the point of trade-off between the cost of debts and benefits of tax shields. They therefore concluded their point by stressing that, tax exemption leads to more debt benefits, debt increases in line with tax exemption, and firm’s value increases with its debt ratio. As plausible as this theory is, its limitations have been pointed out by (Chen & Strange, 2006). The duo faulted the theory for its inability to explain the reasons why capital structure varies across firms of the same tax rate.

From the trade-off theory, the duo of Jensen and Meckling (1976) developed the agency theory in order to argue the relevance of capital structure to firm’s market value. The theory postulates that by trading off the agency cost of debt against the benefit of debt, firms can obtain an optimal capital structure. The theory raises one of the fundamental problems found in an organization-conflict of interest. when a manager’s personal goal competes with the shareholders goal of wealth maximization. Since the administration of the firm’s assets has been conferred on the manager by the shareholders, a potential conflict of interest is bound to exist between the two groups.

To decrease the agency cost between the manager and the employee amidst the conflict of interest, the theory suggest that the manager should use debt rather than free cash flow for the administration of the organization because it is assumed that if managers use debt to run the administration, they will be curtailed from using the free cashflow in appropriately. By so doing, the agency cost between the shareholders and the managers would be reduced. In the same vein, Lubatkin and Chatterjee (1994) opined that jacking up a firm’s debt-to-equity ratio will help shareholders to ensure that managers run the business more efficiently.

Investing in projects with a negative net present value may also be a source of agency cost between bondholders and managers. For instance, investing in projects with higher risk or negative net present value may appears as the best investment to debt finance firms. However, this investment will come with high debt level, increasing cost of capital and bankruptcy costs which can be reduced by limiting the debt ratio by using the short-term debt (Jensen & Meckling, 1976).

For instance, some authors, like DeAngelo and Masulis (1980) criticized Modigliani and Miller’s theory on two distinct bases. they noted that, on the one hand, the theorem is very sensitive to changes in tax code. Which can in turn lead to a better internal leverage decision. While on the other hand, they criticized the fact that the fails to capture the fact that accumulating debt may increase the likelihood of bankruptcy.

In the same vein, another theory that refute the MM theory is the signaling theory. Developed by Ross (1977) the theory criticized the assumption of perfect information as proposed by Modigliani and Miller. The theory’s strength is that in most cases, managers know better that the outsiders, the information asymmetry can work out as a determinant a firm’s capital structure.
Another study that criticized their conclusion to be unrealistic is the study of (Bailey, 2010). The author employed the MM theorem and demonstrated a perfect capital market and concluded that for an investor to repeat the effects of economic behavior as found in corporation, such investor must be able to borrow or source for fund with the same borrowing conditions as the firm.

In other to contribute to extant literature as regards the variables that determines a firm’s capital structure, scholars across the globe have conducted a lot of studies in this regard. While some studies are carried out in emerging economies, some scholars conducted their studies in both developed and developing economies.

Despite this, a review of the findings from these studies suggests that a lot of questions are yet to be answered as regards the debates around the determinants of firm’s capital structure. For instance, Oino and Ukaegbu (2014) submitted that capital structure puzzle remains one of the prominent issues that is gaining a lot of attention from the scholars and policy makers. It is on this basis that the study aimed at contributing to extant literature as regards the determinants of capital structure.

1.3        Statement of the problem

In order to achieve the wealth maximization objective of the firm, many managers have been found to continuously evaluate the various combinations of debt and equity source of finance with the aim of minimizing the average cost capital and maximizing the firm’s market values (Hedau, et al. ,2018). For instance, a firm that sells RM3 million in equity and RM7 million in debts is said to be financed by 20 percent equity and 70 percent debt respectively. Thus, the firm’s debt to total financing ratio is 70 percent. Hence, it is exceptionally important for managers to understand how a firm chooses different its optimal mix of debt and equity capital (Myers, 1984).

This assertion was backed by the pecking order theory that states that firms prefer to finance its operations through the combination of both the internal source of finance such as retained earnings and then, the external source of finance. This theory implies that since it has been argued that the internal source of financing is relatively cheap when compared to the external source, profitable firms will be less likely to finance new projects with external financing since they since there have been provision to finance such project through the internal financing (Hijazi & Tariq, 2006).

A firm can achieve financial stability if it is able to strike balance between the amount of capital available and the amount of capital need (Nasution, Siregar, & Panggabean, 2017). Thus, combining different debt and equity finance in order to yield an optimum capital structure is one of the most challenging tasks being faced by managers across all levels. This is owing to the fact that all financing and funding decision must be done by factoring in the determinants of a firm’s capital structure. While some firms will consider floating their company with equity capital because they perceived it to be cheaper, some many firms may prefer to go for debt financing. However, Nasution, et al. (2017) submitted that a company whose debt-to-capital ratio is very high is said to have a bad financial capital structure and would consequently be hit with a bad financial capital structure. In essence, determining the optimal capital structure along with the appropriate funding composition is very difficult for firms. Thus, beaming the light of research on the determinants of capital structure that can be leverage on will not be out of place.

Financing a firm by debt, equity or the combination of the two constitutes firm’s investment decision and this decision could have its implications both on the shareholder’s earnings or risk. If not properly managed, these implications will affect the firm’s cost of capital and market value. Bearing in mind the implications of the capital gearing, a firm can opt for increasing or decreasing its capital structure by issuing more debt to buy back stock or issuing stock to pay debt. The concern now is “how should a firm choose its debt-to-equity ratio?

In an attempt to examine the nexus that exists between several determinants and capital structure, a lot of researchers have conducted numerous studies. The aim of most these studies was to analyse the impact of several determinants such as firm size, risk, profitability on capital structure. a significant number of these studies revealed that as firm size, risk, profitability significantly influenced capital structure. However, results of these studies in particular to each determinant are contradictory. While some of the determinants exerts a positive influence on capital structure, others exert negative influence on capital structure. in other studies, the influence of these determinants has been found to be statistically insignificant.

For instance, the study of Baloch, et al. (2016), revealed that firm size exerts a positive and significant impact on a firm’s capital structure whereas, the studies carried out by Salim and Susilowati (2019) revealed that not only did firm size exert a negative influence on capital structure, this influence was also found to be insignificant. In congruence to this, the study of Hussain and Miras (2015) revealed that firm size exerts a negatively significant impact on capital structure. it was found in the study carried out by Silva, et al. (2017) that profitability exerts negative influence on capital structure while reverse is the case found in the study carried out by (Jihadi, et al., 2021).

While the study of Ramli and Affandi (2015) found that tangibility have positive and significant effect on firm’s capital structure, the study carried out by Tita (2016) found that tangibility has no effect on the firm’s capital structure. in another instance, Mbonu and Amahalu (2021) found that growth opportunities exert a significant negative effect on debt-to-equity ratio, the study of Hakim and Kasenda (2018) found its effect on capital structure to be positive. Hence, it is imperative to contribute to literature by reexamine these determinants of capital structure with different data.

In addition to this, many of the studies that have been carried out in the past have focused on examining many sectors at time. As good as this could be, it may be difficult to have a great insight into how each sector’s capital structure is being affected by these determinants. Also, not all sectors have the same operations and some sectors are said to be attractive to investors than others. For instance, what constitutes the capital structure of the financial sector may differ significantly from what is obtainable in the manufacturing sector. Therefore, conducting a sector specific study on the determinants of capital structure will yield a robust result.

Based in the researcher’s comprehensive review of extant studies, it has been found that a lot of research has been carried out by numerous scholars as regards the determinants of capital structure. However, a conscious examination of these studies revealed that a greater aspect of these failed to explore the industrial product sector both in the developed or the emerging economies. Same is the case of Malaysia as there is no concrete evidence that a study of this nature has been carried out in the country’s industrial product sector. Therefore, conducting this research with a specific focus on Malaysia’s industrial product sector will help in gaining more insight.
It is on the premise of the following identified gaps that this study purposed to carry out a study with the aim of examining the impact of determinants of capital structure on Malaysia industrial sector.

1.4        Research questions

The broad research question is “what are the determinants of capital structure of companies in the industrial product sector in Malaysia?

In specific:

1. What is the relationship between firm size and debt ratio of companies listed

on Bursa Malaysia?

1. What is the relationship between tangibility and capital structure of companies listed on

Bursa Malaysia?

1. What is the relationship between liquidity and capital strcuture of industrial sector companies

listed on Bursa Malaysia.

1. What is the relationship between profitability and capital structure of industrial sector companies

listed on Bursa Malaysia.

1. What is the relationship between liquidity and capital structure of industrial sector companies

listed on Bursa Malaysia.

1. What is the relationship between growth opportunity and capital structure industrial sector companies listed on Bursa Malaysia.

1.5 Research Objectives

1. To identify the determinants of capital structure in industrial sector of companies listed on Bursa

Malaysia.

2. To investigate the relationship between the capital structure with firm specific characteristics in industrial sector.

In specific:

1. To determine the relationship between firm size and debt ratio of industrial sector companies

listed on Bursa Malaysia.

1. To determine the relationship between tangibility and debt ratio of industrial sector companies listed on Bursa Malaysia.
2. To determine the relationship between liquidity and debt ratio of industrial sector companies

listed on Bursa Malaysia.

1. To determine the relationship between profitability and debt ratio of industrial sector

companies listed on Bursa Malaysia.

1. To determine the relationship between growth opportunity and debt ratio of industrial sector

companies listed on Bursa Malaysia

1.5        Research Process

Figure 1.6.1: The research process

Figure 1.6.1 above presents the chart of the study’s research process. First of all, the study will begin by identifying the problem to be addressed with the problems identification before proceeding to the research question, objectives and hypotheses. The next phases of the study will be used to review relevant literature and selection of the appropriate methodology. The study will further proceed to data collection, analyses and interpretation before moving to the final phase of the study where conclusion will be drawn and recommendations will be made based on the findings.

1.6        Research Hypotheses

Base on the extensive review of literature as regards previous findings and theories, five hypotheses will be purposed in this study to examine the relationship that exist between the dependent (capital structure) and independent variables (firm size, tangibility, profitability, liquidity, and growth opportunity).

H₀: There is a positive relationship between firm size and capital structure.

H₀: There is a positive relationship between tangibility and capital structure.

H₀: There is a negative relationship between profitability and capital structure.

H₀: There is a negative relationship between liquidity and capital structure.

H₀: There is a negative relationship between growth opportunities and capital structure.

1.7        Definition of Variables

Firm Size: this refers to the natural logarithm of firm’s total assets (Abdioğlu, 2019)

Tangibility: this refers to the firm’s assets tangibility and it is measured by taking the ratio of firm’s fixed assets to its total assets (Abdioğlu, 2019).

Profitability: this referred to a firm’s ability to generate profit through sales, total assets, and equity (Salim & Susilowati, 2019).

Liquidity: this is referred to as the level of a firm’s ability to meet its short-term obligations (Jihadi, et al., 2021). It is measured by a firm’s current asset to its current liability.

Growth Opportunities: this refers to firm’s assets that add value to a firm that are not subject to taxable income (Acaravci, 2015). This can be measured by the ratio of market capitalization to book value

Capital structure: this is the ratio of firm’s debt to equity capital (Salim & Susilowati, 2019)

1.8        Significance of the Study

The significance of a study of this nature can never be over emphasized. The study’s significance shall be grouped into practical and theoretical.

1.8.1        Practical Significance

The study will serve as a policy guide the government and corporate stakeholders. On the macro level, the findings from this study will help reveal the relationship between some selected determinants and capital structure. With this, government will be able to formulate informed economic policies that will promote and guide firm’s financial capital structure. on the micro level, the study will aid players in the corporate sector to select the correct determinants of capital structure.

Given the fact that the study is sector specific, the study will offer a clear insight of the appropriate selection of determinants of capital structure to managers and investors in the industrial product sector. This is because different sectors of the economy have different underlying characteristics that differentiate them from each other (Abdul Jamal, Geetha, Mohidin, Abdul Karim, & Ch’ng, 2013). Thus, it may not be totally possible to generalize the determinants of capital structure. Thus. Looking a specific sector will offer a guide to managers and investors in that sector.

1.8.2        Theoretical Significance

The theoretical significance of this studies is that it purposed to fill the existing gaps in literature.

To begin with, the study will contribute to the existing literature by empirically analyzing the determinants of capital in the industrial product sector in Malaysia. This is because a larger part if the study that have been carried out in this regard have focused mostly on the developed economies, and those that did not focus on the developed economy neither considered the industrial product sector, a sector that is important in the growth of Malaysia’s economy.

Furthermore, the findings from this study will contribute to literature as regards the unresolved question as regards the determinants of capital structure as identified in the study’s problem statement. For instance, some studies found that some independent variables are significant to the dependent variables while some studies found otherwise. In the same vein, some studies found that the relationship between some dependent variables and dependent variable are positive while some other studies found these relationships to be negative.

 

1.9        Chapter Summary

This chapter has comprehensively run through some background information about the topic. The chapter has discussed the background to the study alongside the statement of the problem.  The chapter has also anchored the presentation of the study’s broader aim and objectives before proceeding to the research objectives and research question. The chapter also presents the formulation of the study’s hypothesis alongside the chart of the study’s process. The last part of the chapter anchors the study’s definition of terms before discussing the significance of the study.

 

 

 

 

 

 

 

2         CHAPTER 2 LITERATURE REVIEW

2.1        Introduction of Chapter

The aim of this study is to identify the determinants of capital structure of listed companies in Industrial product sector of Malaysia and investigate the impact of each determinant on capital structure of these companies. Hence, the literature presented in this chapter focused on the reviewing extant literature on conceptualization, previous findings and relevant theories as regards the determinants of capital structure.  This chapter will begin with the review of capital structure theories followed by review of related literature of the examined determinants. Furthermore, the hypothesized relationship between each determinant and capital structure will be discussed. The last part of the study will present the theoretical and conceptual framework that will be proposed based on the literature review.

2.2        capital structure Theories

2.2.1        MM theorem:

A cursory look of previous studies have established some underpinning theories of capital structure. The most prominent of these theories is that propounded (Modigliani & Miller, 1958). The duo assumed a flawless market; where there is absence of taxes, no agency costs, no bankruptcy costs, all investors have equal opportunities and information failure does not occur and theorized that the firm’s decision and market value is unaffected by its capital structure. although, this theory has been criticized due to its unrealistic assumption, it helps scholars to think of a new was to start thinking about the capital structure.

To buttress this assumption, Stallkamp (2015) considers two homogenous firms with different capital structure. While firm ABC is financed by equity capital, firm XYZ is financed by debt component. The scholar opined that an investor will be indifferent between the two firms. However, the investor will sale his shares in firm XYZ to buy the same holdings in firm ABC if there is an information that firm ABC is offering a better return. Thus, the firm’s capital structure is irrelevant to influencing the investor’s decision to buy shares of a firm.

2.2.2        Trade-off theory:

This theory is propounded by Myers (1984), this theory propounded that what is left of a levered firm after deducting its financial distress call is equal to the value of a firm without debt plus tax shield.  Thus, a firm will maximize its value by choosing an optical capital structure where trade-off is been made between costs and benefits in relation to the use of the debt and the equity. The firms that operate based on this theory choose their capital structure according to their characteristics.

2.2.3        Agency cost theory:

The proponents of this theory as Jensen and Meckling (1976). The theory posits that a firm can obtain optimal capital structure by trading off the agency cost of debt against the benefit of debt. The theory mention that there are two types of agency costs; the first is the agency cost of equity and the second is the agency cost of debt. While the former is the conflict that exists between the firm’s shareholders and mangers. The latter is the conflict that exists between the firm’s shareholders and debtholders. The assertion made in this theory is that at any point in time, managers of levered firms are under pressure to invest in profitable projects in order to create enough cashflows to pay interest.

2.2.4        Signaling theory:

This theory was propounded by Ross (1977). The theory propose that managers count on inside information to send signals to the market by the choice of their capital structure. If managers believed that their firm is overvalued, they usually issue equity capital and if they believe that their firm is undervalued, managers usually issue debt. Issuing debt covenants by a firm is a positive sign for the market that the firm is confident about future earnings. Issuing debt covenants binds a firm to pay the cost of debt. Failure to pay interest may lead to bankruptcy.

2.2.5        Pecking order theory:

This theory is propounded by Myers and Majluf, (1984). The theory concentrated on the information asymmetry as a determinant of firm’s capital structure because managers knows better than outsiders. The theory proposed that firms follow a specific order in order to finance their projects. Firms will prefer Internal financing to external financing at all time. However, when choosing the external financing, firm will prefer debt financing over equity financing. The theory concluded that only when a firm cannot borrow more debt at a lower cost that equity financing will it issue shares. debt financing raises market perception and in turn increases a firm’s value.

2.2.6        Market Timing Theory:

The theory was propounded by Baker and Wurgler (2002). They theorized that firm’s capital structure is a reflection of its past decisions. The theory states that the cost of debt and cost of equity determines a firm’s choice of financing. A firm will choose debt financing over equity financing only if the cost of debt is lower than the cost of equity as a the time of financing. This will further lead to increase in the firm’s value.

2.3        Reviews on Determinants of Dividend Policy

An extensive review of the findings from previous studies has revealed that capital structure may be determined by firm specific characteristics such as firm size, tangibility, profitability, liquidity and growth opportunities.

Silva, Cerqueira, and Brandão (2017) studied 443 non-financial German companies over the period of 2005 to 2014. The study employed Ordinary Least Squares method and found that both firm size, tangibility and non-debt tax shield exerts positive and statistically significant impact on firm’s capital structure while growth opportunities, profitability, and risk exert negative and significant impact on firm’s capital structure.  The findings from the study supports the main theories of capital structure which are tradeoff theory, the agency theory, the pecking order theory and the market timing theory.

Baloch, Ihsan, Kakakhel, and Sethi (2016) examined 22 firms in Pakistan’s auto sector over the period of 2006 to 2011. The auto sector consists of the following sub-sectors; motor vehicles, trailers and auto parts. The result of the regression analysis revealed that during the period under analysis, the impact of firm size and asset tangibility on financial leverage is negative and significant. Furthermore, the impact of the retained earnings on financial leverage was found to be insignificant.

Noraidi and Ramakrishnan (2018) explored the impact of different firm sizes on capital structure determinants among listed consumer product firms in Malaysia over the period of 2013 to 2015. The study examined 108 of the 130 firms that were listed in Bursa Malaysia. The results of the Pool OLS and Fixed Effect Analysis revealed that different sizes of firm affect the relationship that exists between the determinants of capital structure and firm’s financial leverage. Further, firm size, tangibility, growth opportunities, liquidity, non-tax debt shield and business risk are significant determinants of capital structure across all types of firms.

Nasution, Siregar, and Panggabean (2017) conducted an empirical study on 36 of the Manufacturing Companies listed on the Indonesia Stock Exchange over the period of 2014 to 2016. The study found through the regression analysis that only tangible assets exert positive influence on capital structure while the effect of profitability and non-debt tax shield on capital structure are negative during the period under investigation. Meanwhile, the study found that both corporate tax and inflation has no significant influence on capital structure. it was further revealed that the independent variables for this study jointly influenced financial Capital Structure of the examined companies.

Hussain and Miras (2015) carried out an empirical study in order to analyze the determinants of capital structure of food producing companies in Malaysia. The study examined 45 companies listed under food producer sector at Bursa Malaysia over the period of 2003-2012. The results of the analysis revealed that profitability, firm size and liquidity exert negative and statistically significant impact on capital structure. While tangibility was found to be the only determinant that exert a positive and significant impact on capital structure. Although, the result revealed that growth opportunities have a positive impact on capital structure, this impact is statistically insignificant.

In Malaysia, Wahab and Ramli, (2014) conducted an empirical investigation on the determinants of capital structure measured by book value of total debt ratio and long-term debt ratio of 13 government liked companies over the period of 1997 to 2009. Through OLS multiple regression analysis, the study found that firm size and tangibility are the most significant determinants of capital structure of government liked companies. Furthermore, it was found that the impact of both interest rate and liquidity on the two leverage elements are negative and significant. However, profitability is found to be insignificant as the capital structure determinants. The author concluded that the finding of study is in congruent with most of the previous studies that has been carried out in Malaysia.

The study of Jihadi, et al. (2021) analysed the effect of liquidity, activity, leverage, and profitability on firm value. The purposive sampling techniques was used to select 22 LQ45 index companies that are listed on the Indonesia Stock Exchange over the period of 2014–2019. The result of the Multiple Linear Regression Analysis that was performed through SPSS 18 revealed that profitability, leverage, liquidity and activity have significant impact on firm’s value.

In Turkey, Acaravci (2015) conducted an empirical study on 79 companies listed under the manufacturing sector on the Istanbul Stock Exchange. The study aimed at examining the determinants of capital structure and employed a panel data analysis. The result of the analysis revealed that there exists a joint significant relationship between the explanatory variables and capital structure. Specifically, the study found that growth opportunities have a statistically significant positive effect on capital structure and this is consistent with the trade-off theory. On the other hand, the effect of size, tangibility and profitability on capital structure is negative and statistically significant. They submitted that these findings is consistent with the pecking order theory. However, non-debt tax shield was found to have insignificant effects on capital structure.

In Nigeria, Paseda, (2016) carried out a study with the aim of investigating the determinants of capital structure in Nigeria. The study explored 50 companies from the non-financial corporations listed on the Nigerian Stock Exchange over the period 1999-2014. The study employed a modified panel data least squares regression analysis and found that the influence of asset intangibility, firm age and expected inflation on capital structure is positive and significant. However, the study found that asset tangibility, growth opportunities, firm size, volatility of earnings, profitability, liquidity, dividend-paying status and uniqueness of industry all exert negative influence on capital structure during the period under review. The study concluded that the study result is at best consistent with a mix of pecking order theory, trade-off theory and market condition models.

In Ethiopia, Dessalgne (2018) quantitatively analysed effects of firm specific factors on capital structure by examining the 9 private insurance companies between 2005-2016. The study employed both correlation and multiple linear regression analysis. The correlation analysis revealed that that tangibility, profitability and firm size are significantly correlated with leverage. While the regression analysis revealed that firm size and profitability exerts positive and statistically significant impact on capital structure but the impact of liquidity (insignificant) and tangibility (significant) is negative.  The study further revealed that tangibility, profitability, liquidity and firm size are jointly responsible for 24.5 percent of the variation in capital structure.

Fisseha (2010) carried out an empirical study in order to analyze the determinants of capital structure of commercial banks in Ethiopia over the period of 2000-2009. The study found through the multivariate regression analysis that profitability and growth exert significant negative impact on capital structure while tangibility, firm size, age and tax-shield all exert positive impact on capital structure. More importantly, the study concluded that the impact of profitability on capital structure is consistent with the Pecking order theory, while the result of tangibility on capital structure supports Trade-off theory, Pecking order theory and Agency cost Theory. In addition to this, the impact of firm size and growth opportunities on capital structure is consistent with both Trade-off theory and Agency cost Theory while the impact of age and tax-shield on capital structure is consistent with Static Trade-off Theory.

Accordingly, Thomas, Chenuos and Biwott (2014) conducted a study aimed at analyzing the effect of firm specific determinants on capital structure. The determinants identified for the study are firm size, liquidity and profitability. The study explored 34 firms listed in Nairobi Securities Exchange over the period of 2006-2012. It was found through the analysis that capital structure has negative and statistically significant relationship with profitability, and liquidity. However, the relationship between firm size and capital structure is positive but statistically insignificant.

Suarez (2016) conducted a study in order to determine the nexus that exists between tangibility, corporate taxes, age, firm size and capital structure of firms in Columbia’s industrial sector over the period of 2011 to 2012. The study examined 35 out of the 44 companies listed on the Colombian Stock Exchange. The result obtained from the correlation analysis revealed that capital structure exerts no significant relationship with tangibility, corporate tax rate, and age of companies. However, a positive and significant relationship was found between firm’s size and its capital structure which is consistent with the trade-off theory.

Akdal (2011) carried out research with the aim of determining the influence of various firm level characteristics on capital structure. The study examines 202 of the 250 UK listed companies over the period of 2002-2009. The multiple regression revealed that the impact of profitability and liquidity on capital structure are negative and statistically significant. Conversely, the study found that the impact of asset tangibility on capital structure is a positive and statistically significant. Moreover, the findings revealed that capital structure can best be proxied by total debt ratio at market value of equity and long-term debt ratio at market value of equity respectively.

By examining all 64 firms listed on the Nairobi Securities Exchange over the period of 2010 to 2014, the study carried out by Marete (2015) sought to establish the effect of firm size, sales volume and profitability on the financial leverage of these firms. The financial leverage was measured by debt-to-equity ratio and the study was based on the premise of trade off and pecking order theory. The study found through the analysis that firm size exerts a significant positive effect on financial leverage. Furthermore, the study found that the impact of profitability and sales volume on financial leverage is negative and statistically significant.

The quantitative research carried out by Corina, RiaMurhadi, and Wijaya (2017) attempts to examine the impact of firm size, profitability, tangibility, liquidity and growth on financial leverage. The study examined 328 firms across all sectors that are listed over the period of 2011 to 2015. The study employed multiple linear regression model to analyse the panel data that comprise of 1640 observations. The result of the analysis found that the impact of tangibility and firm size on financial leverage is positive and statistically significant. While the impact of profitability, growth, and liquidity on financial leverage is found to be negative and statistically insignificant during the period under analysis.

The study carried out by Tita (2016) sought to examine the effect of the following firm characteristics; firm size, profitability, tangibility, non-debt tax shield and sales growth of the capital structure. The study focused on the Indonesia’s banking firms and examined 21 banks listed on Indonesian Stock Exchange over the period of 2007-2012. The findings from the analysis revealed that firm size, profitability, growth and non-debt tax shield has effect on capital structure, while tangibility was found not to have any effect on capital structure during the period under analysis. Meanwhile, study found through the value of R-squared that the independent variables jointly explained 87.4 percent of the variation in capital structure.

In an attempt to analyse which of the capital structure theories is the Industrial Malaysia Sector consistent with, Ramli and Affandi (2015) carried out a panel data analysis on 106 companies in Malaysia over the period of 2003 to 2012. The result of the analysis revealed that except for tangibility, the explanatory variables jointly exact significant impact on capital structure. specifically, it was found that firm size and tangibility has a positive influence on capital structure while profitability and liquidity have a negative influence on capital structure. Based on the findings, the study concluded that the Malaysia’s industrial product sector is consistent with both trade-off theory and pecking order theory.

In the same vein Sakr (2018) carried out a study in Egypt with the aim of examining the determinants of capital structure at the industry and country level. The study focused on 58 Egyptian publicly traded non-financial listed firms over the period of 2003 to 2016. Based on the empirical evidence, the study concluded that the choice of capital structure of the sampled companies was consistence with both trade-off theory an pecking order theory.

Gharaibeh and AL-Tahat (2020) examined the determinant of capital structure 45 service companies in Jordan over the period of 2014 to 2018. The study employed the panel regression approach for the data analysis and found that the impact of firm size and non-debt tax shield on capital structure is positive and statistically significant, while that of profitability and business risk on capital structure is negative and also significant. Meanwhile, the impact of growth opportunities on capital structure was found to be negative but insignificant. Generally, the findings revealed that capital structure determinants (firm size, profitability, business risk and non-debt tax) jointly have an effect on debt ratio which is used as a proxy for capital structure. The study concluded that the findings of the analysis is consistent with the trade-off, bankruptcy cost, agency cost and pecking order theories.

Mbonu and Amahalu (2021) carried out an Ex-Post Facto researcher with the aim of ascertaining the effect of firm characteristics on capital structure. the study focused on insurance companies listed on Nigeria Stock Exchange over the period of 2011-2020. A purposive sampling technique was used to select 14 insurance companies. The study found that the effect of firm size on capital structure is positive and statistically significant while that of revenue growth and liquidity is negative and significant. The study thus recommends, amongst other things, that the listed insurance companies in Nigeria should strive to attain and maintain a sound asset base in order to keep performing their roles the customers.

In Pakistan, the factors that determines capital structure of the firms in the sugar industry was examined by Awan, Faridi, and Ghazi (2016). The aim of the empirical study was to ascertain whether or not will the study’s result be consistent with the trade-off theory and pecking order theory. The study employed a panel data technique to study 30 listed firms on the Karachi Stock Exchange over a period of 2008-2011 and found that the effect of tangibility and profitability on capital structure is positive and significant while that of firm size and liquidity are negatively and significant. Meanwhile, the effect of growth and tangibility on capital structure are insignificant. The study concluded that the effect of the profitability and tangibility is consistent with the trade-off theory while that of liquidity, size and growth are consistent of pecking order theory.

In Zimbabwe, Hove and Chidoko (2012) employed a balanced panel data over the period of 2000 to 2008 to empirically examine the major determinants of capital structure of multinational corporations listed on the Zimbabwe Stock Exchange. Through the panel data regression analysis, the study found that the impact of tax, tangibility and size on capital structure is positive and significant while the impact of non-debt-tax-shields and profitability on capital structure is negative and significant.

2.4        Literature Summary

Table 2.1 below presents the summary of the review of empirical studies that examined the relationship between dependent variable (capital structure) and independent variables

The abbreviations are listed as below:

FSZ : Firm size

PRO : Profitability

TAN: Tangibility

GRO: Growth opportunities

LQY : Liquidity

+ : Positive relationship

– : Negative relationship

x : Insignificant

 

Table 2.1: Summary of Empirical Studies

Author (s)	Studied country	Studied Sector	Studied Period	Methodology	Relationship with capital structure
					FS	P	TA	GO	LI
Silva, Cerqueira, and Brandão (2017)	Germany	Non-financial Listed German Companies	2005 to 2014	Ordinary Least Squares.	+	–	+
Baloch, Ihsan, Kakakhel, and Sethi (2016)	Pakistan	Auto sector	2006 to 2011	multiple regression	–		–
Noraidi and Ramakrishnan, (2018)	Malaysia	Consumer product sector	2013 to 2015	Pooled OLS and Fixed Effect Analysis	+	–	+		–
Nasution, Siregar, and Panggabean (2017)	Indonesia	manufacturing companies in the consumption goods sector	2014 to 2016	descriptive statistics, tests of classical assumption as and multiple linear regression		–	+
Hussain and Miras (2015)	Malaysia	food producer sector	2003-2012	Pearson correlation coefficient and multiple linear regressions	–	–	+	X	–
Wahab and Ramli, (2014)	Malaysia	Government linked Companies	1997 to 2009	Pearson correlation and regression analysis	–	X	+		–
Jihadi, et al. (2021)	Indonesia	22 LQ45 index companies	2014-2019	Multiple Linear Regression
Acaravci (2015)	Turkey	manufacturing sector	1993 to 2010	Fixed Effects Model Analysis	–	–	–		+
Paseda, (2016)	Nigeria	Non-financial corporations	1999-2014	Panel data least squares regression	–	–	–	–	–
Dessalgne (2018)	Ethiopia	private insurance companies	2005-2016	multiple linear regression	+	+	–		X
Fisseha (2010)	Ethiopia	commercial banks	2000-2009.	multivariate regression analysis	+	–	+	–
Thomas, Chenuos and Biwott (2014)	Nairobi	corporate financial firms excluding commercial banks	2006-2012	Pearson Correlation and multiple regression	X	–			–
Suarez (2016)	Columbia	Industrial sector	2011 to 2012	Pearson correlation	+		X
Akdal (2011)	UK	202 companies from FTSE	2002-2009	multiple regression		–	+	X	–
Marete (2015)	Nairobi	companies listed Nairobi Securities Exchange	2010 to 2014	regression analysis and Pearson’s Product Moment Correlation analysis	+	–
Corina, RiaMurhadi, and Wijaya (2017)	Indonesia	non-finance sectors	2011 to 2015.	multiple linear regression	+	X	+	X	X
Tita (2016)	Indonesia	Banking Firms listed in Indonesian Stock Exchange	2007-2012	panel data regression with fixed effect model	+	–	X	–
Ramli and Affandi (2015)	Malaysia	Industrial product sector	2003 to 2012	Ordinary Least Square Regression and panel least square regression analysis	+	–	X		–
Sakr (2018)	Egypt	Egyptian publicly traded non-financial firms.	2003 to 2016	Pearson Correlation Coefficients and OLS Regression Results		–
Gharaibeh and AL-Tahat (2020)	Jordan	service companies	2014 to 2018	panel regression	+	–	X	X
Mbonu and Amahalu (2021)	Nigeria	Insurance Companies	2011-2020	Pearson correlation coefficient and Panel least square regression analysis	+			–	–
Awan, Faridi, and Ghazi (2016).	Pakistan	Non-Financial Companies	2008-2011	panel least square, fixed effects and random effects.	–	X	+	X	–
Hove and Chidoko (2012)	Zimbabwe,		2000-2008	panel data regression analysis	+	–	+	X	X

 

 

 

2.5        Hypothesis Development

The aim of this study is to identify determinants of capital structure of the companies listed in the industrial product sector in Malaysia over the period of 2016-2021. Upon the comprehensive review of related literature on determinants of capital structure, five explanatory and one dependent variable are selected. The chosen explanatory variables are firm size, profitability, tangibility, liquidity and growth opportunities.

A lot of empirical studies have been carried out in the past as regards these variables and the findings from these studies have revealed that each of these determinants have conflicting impacts on capital structure. While some are found to be positive, some are found to be negative relationship and some have even been found to be irrelevant to capital structure. Therefore, it is pertinent to reexamine the association that exists between the identified determinants and capital structure. Hence, this current study seeks to provide fresh empirical evidence and add clarity.

This section discusses the formulation of hypothesis as regards the relationship that exist between each identified explanatory variables and capital structure. Each hypothesis formulation shall be justified by findings from previous studies and capital structure theories.

2.5.1        Firm Size

According to Ramli and Affandi (2015) a firm’s size can be classified into three categories: small firm, medium firm, and large firm. regardless of its category, firm size is usually measured by the firm’s total asset due to the fact that it can easily reveal a firm’s ability to carry on its operation.

The trade-off theory Jensen and Meckling (1976) supports the hypothesis that there exists a positive relationship between size and capital structure of a firm. This theory posits that large firms have easy access to borrow funds more than small firms because they are more diversified, have lower agency cost and are relatively faced with lower financial distress (Silva, Cerqueira, & Brandão, 2017). Other than that, (Harris & Raviv, 1991) confirmed that the pecking order theory (Myers & Majluf, 1984) sustained the positive relationship between size and firm’s capital structure because larger firms offer more information to market players. Larger firms obtain new and more funding from creditors.

A lot of studies that have been carried out in the past as regards the influence of firm size on capital structure has revealed different results. While some found positive results, some studies found negative result and some found insignificant result.  Among the previous studies that found a positive association between firm size and capital structure are that of (Dessalgne, 2018), (Fisseha, 2010), (Suarez, 2016), (Marete, 2015), (Corina, RiaMurhadi, & Wijaya, 2017). In the same vein, firm size has been found to exerts negative relationship with capital structure in the previous studies of (Hussain & Miras, 2015), (Baloch, Ihsan, Kakakhel, & Sethi, 2016), (Paseda, 2016). Meanwhile, some studies have also found that firm’s size is insignificantly associated with capital structure. Among the previous studies that have found this irrelevant association is (Thomas, Chenuos, & Biwott, 2014).

In essence, majority of the previous studies that have been carried out in respect of firm size and capital structure have provided empirical evidence that suggest positive relationship between firm size and capital structure. large firms are less risky which makes them to have more debt capacity since they can borrow more at lower interest rates (Lee, Koh, & Kang, 2011) While small firms will not engage in external funding because they are financially limited enterprises constrained with increased expenses.

In short, most of the researchers provided empirical evidence on the positive relationship between firm size and dividend policy. Therefore, size of Malaysia listed firms is expected to be positively related with capital structure and this forms the basis for the study’s first hypothesis.

H1: There is a positive relationship between firm size and capital structure of industrial sector companies listed on Bursa Malaysia.

2.5.2        Tangibility

Tangibility according to Baloch, Ihsan, Kakakhel, and Sethi (2016) is the degree to which a company is financed by long term assets such as plant, machine, buildings etc. This is usually measured by taking the ratio of a firm’s fixed assets to its total assets. Companies with more tangible assets are less prone to financial distress because they maintain more sales revenue through higher level of production (Akintoye, 2009). On the other hand, more tangible assets means that companies have low leverage which exposes them to financial distress because it will be difficult for them to use such assets as collateral to generate more funds (Baloch, Ihsan, Kakakhel, & Sethi, 2016).

Wahab and Ramli (2014) posits that firms with more tangible assets have the potential to use more debt because the assets can be employed as collateral to reduce the cost of financial distress. This is consistent with the agency cost theory propounded by Jensen and Meckling (1976) where it was stated that firms can use increase in the value of tangible assets to reduce the agency cost of debt by pledging these tangible assets as collateral.

The review of previous studies conducted in this regard have found mixed results. Some studies found positive association between tangibility and capital structure while others sound this association to be negative. Meanwhile, some studies have found this association to be inconsequential.

Amongst the studies that found positive association between tangibility and capital structure are (Silva, Cerqueira, & Brandão, 2017); (Nasution, Siregar, & Panggabean, 2017), (Hussain & Miras, 2015), (Fisseha, 2010); (Akdal, 2011), (Corina, RiaMurhadi, & Wijaya, 2017), (Ramli & Affandi, 2015), (Awan, Faridi, & Ghazi, 2016), (Hove & Chidoko, 2012). However, the research outcome of (Baloch, Ihsan, Kakakhel, & Sethi, 2016); (Acaravci, 2015), (Dessalgne, 2018); found a negative relationship between tangibility and capital structure. however, (Awan, Faridi, & Ghazi, 2016); by (Tita, 2016) found that tangibility is inconsequential to capital structure.

To sum up, in view of the positive relationship between tangibility and capital structure in accordance with capital structure theories and empirical evidences, this study expects tangibility will exert positive influence on capital structure. H2 is developed as below:

H2: There is a positive relationship between tangibility and capital structure.

2.5.3        Profitability

Profitability of a firm is considered as a key factor in determining a firm’s capital structure. To measure a firm’s profitability ratio, different financial ratios can be employed. Amongst these financial ratios are Return on Equity, Gross Profit Margin, Return on Asset, and Net Profit Margin (Salim & Susilowati, 2019). Although, it is usually measured by the firm’s ratio of net income to its total assets (Abdioğlu, 2019). Profitability has been cited as one of the important determinants of capital structure. Firms acquire more debt as a means of curbing managers from wasting free cashflow generated through profits. As a result of this, access to more debt becomes easier because the higher the firm’s profit level, the higher its debt capacity (Silva, Cerqueira, & Brandão, 2017).

Profitability interacts with financing decisions (Danis, Rettl, & Whited, 2014). This is in line with the Pecking order hypothesis where inverse relation was proposed between leverage and profitability because more profitable firms do not have reasons to borrow to cater for their capital expenditures. However, the trade-off model hypothesizes that more profitable firms will seek to maximize their tax benefits through increased leverage.

In order to offset corporate taxes, profitable firms should maintain a high leverage ratio (Silva, Cerqueira, & Brandão, 2017). This assertion is supported by the trade-off theory where it is stated that higher profitability leads to a higher level of debt. On the other hand, (Campbell, Hilscher, & Szilagyi, 2008) posits that a firms with low profitability ratio are exposed to higher level of financial distress making it difficult for such firm to acquire debt. Handoko (2017) opined that by relying on internal source of financing. Firms with higher profitability level will relatively reduce usage of debt to finance their operation. This is supported by the negative relationship posits by the pecking order theory Myers and Majluf (1984) where it was stated more profitable firms tends to use their own capital more than firms with lower profitability because it is relatively cheaper.

The studies previously conducted by various scholars in order to examine the association that exists between profitability and capital structure have come up with different conflicting results. While some studies found this association to be positive, some studies found it to be negative. Meanwhile, the findings of some scholars have even found this association to be irrelevant.

Amongst the studies that found positive association between the two are those of Dessalgne (2018) and (Awan, Faridi, & Ghazi, 2016). Conversely, a lot of findings from the previous empirical studies revealed a negative relationship between profitability and capital structure. Amongst these studies are those conducted by (Hove & Chidoko, 2012), (Gharaibeh & AL-Tahat, 2020), (Ramli & Affandi, 2015), (Corina, RiaMurhadi, & Wijaya, 2017), (Marete, 2015); (Akdal, 2011); (Thomas, Chenuos, & Biwott, 2014), (Fisseha, 2010); (Paseda, 2016); (Hussain & Miras, 2015).
To sum up, in view of the positive relationship between profitability and capital structure in accordance with capital structure theories and empirical evidence, this study expects profitability to exert negative influence on capital structure. H2 is developed as below:

H2: There is a negative relationship between profitability and capital structure.

2.5.4        liquidity

A firm’s liquidity is one of the key determinants of capital structure. it reveals the firm’s ability to meet its financial obligations as at when due by converting its assets into cash without affecting its market value (Ross, Westerfield, & Jaffe, 2010). When a firm is able to fulfill its financial obligation at maturity, such firm is said to be liquid (Jacob & Taslim, 2017). Salim and Susilowati (2019) opined that firms are to maintain healthy liquidity ratio because of its implication on funding activities. For instance, firms with higher level of liquidity are said to have enough liquid assets to finance its debt. Hence, they face a lower cost of borrowing (Hassan & Samour, 2015).

Furthermore, highly liquidity assets lead to more favourable working capital which will in turn allow firm to save up funds for financing long-term investment without borrowing from external source (Hamzah & Marimuthu, 2018). This was supported by the pecking order theory that suggested a negative relationship exist between liquidity and capital structure because whenever the firm requires urgent cash, they can easily liquidate their current assets in order to fulfill its obligations (Md-Yusuf et al., 2013).

Conversely, the tradeoff theory suggested a positive relationship between liquidity and capital structure and suggest that firm with highly liquid assets should acquire more debt. This is because of their ability to fulfill their contractual obligations on time by converting their liquid assets (Berkman, Iskenderoglu, Karadeniz, & Ayyildiz, 2016).

The studies conducted in the past as regards the influence of liquidity on capital structure have come up with mixed results. Studies such as (Hussain & Miras, 2015), (Thomas, Chenuos, & Biwott, 2014), (Akdal, 2011), (Mbonu & Amahalu, 2021) found a negative influence between liquidity and captial structure. However, the relationship found by the studies of (Dessalgne, 2018), (Corina, RiaMurhadi, & Wijaya, 2017) found positive influence.

On the basis of the negative relationship found between liquidity and capital structure in accordance with capital structure theories and empirical evidence, this study expects liquidity to exert negative influence on capital structure. H2 is developed as below:

H2: There is a negative relationship between liquidity and capital structure.

2.5.5        Growth opportunities

Growth opportunities is another key determinant of capital structure that is used to measure firm’s opportunities to expand its business growth through new investment opportunities (Silva, Cerqueira, & Brandão, 2017). it represents firm’s capital assets that add value to a firm, has no collateral value and is not subjected to taxable income (Titman & Wessels, 1988).
(Deesomsak, Paudyal, & Pescetto, 2004) growth opportunities to be negatively related with leverage level of firms in Thailand and Singapore. The authors argued that firms with high growth opportunities prevents being exposed to creditor’s rules and regulation by avoiding the issuing of debt. This is in consistence with Jensen and Meckling (1976), Myers and Majluf (1984) where it was argued that firms that acquires more debt tends to miss out on profitable investment.

Thus, firms with high future opportunities should employ more equity financing. on this basis, the trade-off theory suggests a negative association between capital structure and growth opportunities. This is on the basis that firms with investment opportunities have less capital structure because they have more substantial incentives to avoid asset substitution and under-investment that can arise from stockholder-bondholder agency conflicts.

On the other hand, Hamzah and Marimuthu (2018) argued that there is a positive relationship between growth opportunities and leverage level. This is in consistence with the pecking order theory where it was suggested that growth opportunities drive investment and the fund to finance these opportunities may not be sufficiently available to firms. Thus, firm may have to acquire more fund to finance this growth opportunities in order not to lose out on the investment.

A lot of studies that have been conducted in order to examine the association between growth opportunities and have found mixed results. Some authors have found positive association while others have found negative association. Meanwhile, some studies have also come up with an inconsequential association between the two. The studies of (Silva, Cerqueira, & Brandão, 2017), (Paseda, 2016), (Mbonu & Amahalu, 2021) and (Fisseha, 2010) are amongst those that found negative impact between growth opportunities and firm’s capital structure. on the other hand, the study carried out by (Acaravci, 2015) found growth opportunities to be positively related with capital structure. however, insignificant relationship was found to exist between growth opportunities and capital structure by (Hussain & Miras, 2015) (Corina, RiaMurhadi, & Wijaya, 2017).

On the basis of the negative relationship found between growth opportunities and capital structure in accordance with capital structure theories and empirical evidence, this study expects growth opportunities to exert negative influence on capital structure. H2 is developed as below:

H2: There is a negative relationship between growth opportunities and capital structures.

2.6        Theoretical Framework

The selected independent variables are assumed to be related with firm’s capital structure. Thus, each each variable is hypothesized to exert either positive or negative influence on capital structure according to the review of extant literature and theories. Figure 2.1 below depict the study’s current theoretical framework

Figure 2.1 theoretical framework

 

 

 

2.7        Conceptual Framework

Five distinct hypotheses, shall be tested in this study. Their basis of formulation has been thoroughly discussed in the previous section. The H1 through H5 represents the alternative hypothesis that tests the relationship between the explanatory variables and dependent variable. Each individual hypothesis is carefully formulated based on the review of relevant theories and empirical evidence on the determinants of dividend policy. The dependent variable in this study is capital structure while the explanatory variables that are expected to be the determinants of capital structure are firm size, tangibility, profitability, liquidity and growth opportunities.

Figure 2.2 below presents the proposed conceptual framework for this study.

H4

H5

Figure 2.2 Proposed conceptual framework

2.8        Chapter Summary

This chapter of the study ran through the review of extant literature in line with the determinants of capital structure. Firm size, profitability, tangibility, liquidity and growth opportunities are the selected independent variables while capital structure is the dependent variable for this study. The study reviewed different theories propounded in relation to capital structure along side the empirical studies of various authors. The review of empirical studies revealed that opposing results were found as regards the relationship between the dependent and the independent variables.

This chapter has identified that a lot of studies that have been carried out to investigate the impact of capital structure determinants have not focused on the industrial product sector of Malaysia Economy. Thus, the study seeks to fill this gap. Five distinct hypotheses were formulated based on majority of findings and according capital structure theories. the chapter also presented the theoretical and conceptual framework. The theoretical framework depicts the association between the independent variables and capital structure theories while the conceptual framework depicts the relationship between the dependent and independent variables. The next chapter of the study will anchor the study’s research methodology.

 

3         CHAPTER 3 RESEARCH METHODOLOGY

3.1        Introduction of Chapter

This chapter will discuss the study’s research design. It the chapter will start with a detailed description of the research design before proceeding to the study’s target population and sample size. The sampling technique and data measurement are also addressed before moving to discuss the types of data analysis approaches for this study. The last part of this chapter summarized the whole chapter.

3.2        Target Population and Sample Size

The target population for this study shall consist of 432 firms under the industrial product sector that are listed on the Bursa Malaysia stock exchange. Secondary data for the analysis will be collected from the selected firms over the period of 2016-2021. However, all these firms shall be subjected to some certain criteria before they can be included in the sample.

The firms from the Bursa Malaysia stock exchange shall be subjected to further screening based on some criteria:

1. Firms with complete data for all the years are available shall be included.
2. Only firms enlisted on the Bursa Malaysia stock exchange market during 2016-2021 will be included.
3. The firms that continuously use leverage in their capital structure during these years shall be included.
4. Firms whose financial year ends by 31^st December only will be included for this analysis.
5. Data is available for all variables identified in the study. If a firm has missing data on one or multiple variables, all the observations of that firm will be eliminated. This will facilitate a multivariate analysis.
6. The companies did not change their fiscal year for the period studied.

After the identification and elimination of outliers in this data, the estimation will be based on 552 out of 552 observations from 92 firms.

3.3        Sampling Technique

When trying to select a sample as a representative of a population, the role of sampling techniques is very important. This is because it is one of the most important factors that determines the accuracy of the result obtained in a study since the effect of including bad sample in a study will reflect in the analysis. Generally, there are two major types of sampling techniques. The probability and non-probability sampling techniques (Aliaga & Gunderson, 2000).

The probability technique is a systematic way of selection where every member of the population has equal chances of being selected as a representative for the analysis. This type of sampling technique consists of simple random, systematic, stratified and cluster sampling (Greene, 2003). On the other hand, the non-probability technique is a selection method where samples are selected based on some purposive criteria rather than in a systematic way. This type of sampling technique consists of the convenience, purposive or judgmental, quota and snowballing sampling. For the purpose of this study, the judgmental sampling technique will be adopted. This is because it will permit the researcher to select only the firms that meet up with the selection criteria (Kothari, 2004).

The rationale behind the adoption of judgmental sampling method for this study is because it is a technique that permits the researchers to select samples based on the selection criteria that have been stated above. In addition to this, this sampling method found to be cost and time effective.

3.4        Data Measurement

Independent variables	Proxy	Symbol	Description
Firm size	Natural logarithm of total assets	+	FSZ = Log (Total assets)
Profitability	Return on assets	–	PRO = Net income/ Total assets
Tangibility	Ratio of fixed assets to the total assets	+	TANG= Fixed Assets/Total Asset
Growth opportunities	Market-to-book ratio	–	GRO= Market capitalization/Book value
liquidity	Current Ratio	–	LQY=Current Asset/ Current Liability
Dependent variables	Proxy		Description
Capital structure	Debt-to-equity ratio		CPS=Total debt/Total Asset

Table 3.1: Measurement of Variables

3.5        Data Collection Method

Research design is the overall strategy and procedural outline that is chosen in a study in order to fulfil the research objectives in a logical way. This study will adopt a quantitative research design in order to examine the relationship between capital structure and its determinants. The rationale for adopting this research design over others is that the result obtained from this method enables the researcher to describe, predict, forecast and generalize a phenomenal by objectively gathering and computing numerical data.

Furthermore, the study will apply secondary data collection method in order to offer new insight from what has been found in previous studies. One advantage of secondary data source ahead of primary data collection method is that it is relatively easy to access, readily available (mostly on the internet) and absolutely free. Thus, for convenience, easy accessibility and timeliness, the secondary data for this analysis will be sourced from companies’ annual reports, financial statements and financial databanks like investing.com and Morningstar amongst others.

3.6        Analysis Tools

3.6.1        Descriptive Analysis

Descriptive analysis is a form of data analysis that is carried out in order to understand the patterns of each sample in a study. Since it will be difficult and unscientific to make judgments on the pattern of raw data, conducting a descriptive analysis on raw data cannot be overemphasize. it aids and facilitates data analysis and interpretation by measuring frequencies, measure of central tendencies such as mean, median and mode; measure of dispersion such as range, maximum, minimum, variance and standard variation.

3.6.2        Correlation Analysis

Correlation analysis is a statistical tool used for investigating the existence or nonexistence of relationship between two or more variables. in addition to this, it also quantifies the degree or extent of this relationship. Conducting this statistical analysis is essential because it aids the understanding of the nature and degree of relationship that exists between two or more variables.

The r coefficient ranges between -1 to +1. When the value of r coefficient is -1, negative correlation is said to exist between the variables. when the r coefficient results in a value of +1, the relationship between the variables is said to be positive. Meanwhile, a r coefficient of 0 (zero) implies that no correlation between the two variables. it was noted that while the negative and positive signs of the r coefficient reveal the direction of the relationship, the absolute value reveals the degree of the relationship (Zao, Tuncali, & Sliverman, 2003). When the correlation coefficient is positive, it infers that a rise in one variable will lead to a rise in the other variable. on the other hand, a negative correlation coefficient infers that a rise in one variable leads to a fall in the other variable.

3.6.3        Multiple Regression Analysis

Multiple regression is one of the data analysis techniques that extends the linear regression that accommodates numerous independent variables into an equation in order to examine their effect on the dependent variable (Gujarati & Porter, 2009). Simply put, multiple regression is a statistical analysis that reveals the linear effect of more than one explanatory variables on the dependent variable. For this study, Multiple regression analysis will be conducted on the panel data (combination of Time series and cross-sectional data) in order to examine the impact of the selected explanatory variables on capital structure of firms under the industrial product sector in Malaysia over the period of 2016-2021. The econometric model for the study shall consist of five independent variables and one dependent variable.

CPS = α + 𝛽1 FSZ + 𝛽2 PRO + 𝛽3 TANG + 𝛽4 GRO + 𝛽5 LQY

Where:

CPS = Debt to equity ratio

α = the constant

β1- β5 – = The coefficient of independent variables

FSZ = The natural log of total assets

PRO = Net income/ Total assets

TAN = Fixed Assets/Total Asset

GRO = Market capitalization/Book value

LQY = Current Asset/ Current Liability

ϵ = the model’s random error term

3.6.4        Panel Data Analysis

Generally, panel data analysis is a statistical method used that is commonly used to analyse two-dimensional panel data. Data is said to be panel data when is consist of observations collected from cross-sections over certain periods of time. One of the advantages offered by this type of analysis is that it minimizes the estimation biases that comes as a consequence of condensing groups into a single time series. Other than that, panel data aids the development of more efficient estimators and also facilitates the reduction of collinearity among the independent variables (Silva, Cerqueira, & Brandão, 2017). Panel data analysis can be conducted through with the following estimation techniques:

3.6.5        Pooled Ordinary Least Squares Model (OLS)

Generally, ordinary least squares (OLS) is one of the estimation techniques that is being employed in order to estimate the impact of one or more explanatory variables on a dependent variable. This is because it is unbiased, consistence and has constant variance. Meanwhile, pooled OLS estimation have been identified as one and the same with the application of OLS methodology on panel data because it assumes similar intercept and regression coefficients for all cross sections which may result in biased and inconsistent estimate of the coefficient (Gujarati & Porter, 2009).

3.6.6        Fixed Effects Model

Unlike the pooled OLS, fixed effect model permits different intercept in regression for all cross-sectional. Within an entity, a fixed effects model is used to analyse the influence of the explanatory variables on the outcome variable. One key assumption of this model is that the explanatory variables are biased or influenced by individual features Therefore, this model reveals how individual characteristics may exert impact on the explanatory variables. One important advantage of this model is the net effect of the explanatory variables on the dependent variable can be determined by sidelining the effect of specific time-irrelevant features such as age, gender, culture and religion amongst others (Silva, Cerqueira, & Brandão, 2017)..

The equation of fixed effects model is illustrated as below:

𝑦𝑖𝑡 = 𝛽1𝑋𝑖𝑡 + 𝛼𝑖 + 𝑢𝑖𝑡

Where:

𝑦𝑖𝑡 = the debt-to-equity ratio where i = entity and t = time

𝛽1 = the coefficient of explanatory variables

𝑋𝑖𝑡 = the explanatory variables

𝛼𝑖 = the intercept for each entity

𝑢𝑖𝑡 = the error term

3.6.7        Random Effects Model

Random effects model is employed in a panel data analysis where the assumption of fixed effects is relaxed. Based on the assumption that the heterogeneity is not correlated with the explanatory variables used in the model, random effects model helps to control unobserved heterogeneity.

One of the advantages of this model is that it permits the inclusion of time-invariant variables. These variables can be used as explanatory variables since the entity’s error term is believed not to be correlated with the independent variables (Gujarati & Porter, 2009).

The equation of random effects model is written as:

𝑦𝑖𝑡 = 𝛽𝑋𝑖𝑡 + 𝛼 + 𝑢𝑖𝑡 + ϵ𝑖𝑡 Where:

𝑦𝑖𝑡 = the debt-to-equity ratio

𝛽 = the coefficient of independent variables

𝑋𝑖𝑡 = the explanatory variables

𝛼 = the intercept 𝑢

𝑖𝑡 = the error between entity

ϵ𝑖𝑡 = the error within entity

3.6.8        Hausman Specification Test

Hausman test is commonly used in a panel analysis to determine whether to employ the fixed or random effects model in a study. When formulating the hypothesis, random effects model have been widely preferred as the null hypothesis whilst the fixed effects model commonly favoured as the alternative hypothesis. In essence, the test is carried out to test if there is any nexus between the specific mistakes and the regressors in the model. The decision rule for this test is simple and straightforward. If the probability value is below 0.05, the null hypothesis of random effect should be rejected and the fixed effects model should be employed. If otherwise, we do not reject the null hypothesis or random effects (Gujarati & Porter, 2009).

The hypotheses are formulated as follows:

𝐻0: Random effects model is appropriate.

𝐻1: fixed effects model is appropriate.

3.6.9        Robustness Test

When conducting an empirical analysis, the role of robustness check cannot be overemphasized. This test is generally used to test how the introduction or elimination of regressors due to change in model specification will affect the regression coefficients. When the data series for the analysis contains too many outliers, the OLS model will produced a spurious result that may be difficult to interpret.

3.6.10    Multicollinearity Test

The issue of multicollinearity is said to be present in a regression model when two or more explanatory variables linearly correlated. This implies that the measures of two or more explanatory variables are so related that there is virtually no distinction between their results. There are various methods of testing for the presence of multicollinearity in a model. However, this study will employ the variance inflation factor (VIF) and correlation matrix will also be used as indictors of multicollinearity. When the VIF value is greater than 10, researchers always conclude that there is presence of multicollinearity in the model. although, in weaker models, VIF values that are greater than 4 can also e of concern. In addition to this, a correlation coefficient greater than 0.7 between two or more explanatory variables is also said to be indicating the presence of multicollinearity in a mode (Greene, 2003).

3.7        Chapter Summary

This chapter covered the study’s research design, discussed the target population alongside the sample size and sampling techniques. The study will employ quantitative research method where secondary data will be sourced for the analysis. The measurement of data as well as the sources of data collection was equally discussed in this chapter. The various analysis techniques that shall be used are discuss. More importantly, all the analysis that shall be carried out in this study will be through EViews. The next chapter will present the study’s analysis and interpretation.

 

 

 

 

 

 

4         CHAPTER 4 DATA ANALYSIS AND FINDINGS

 

4.1        Introduction of Chapter

The collected data sample will be analyzed by using several analysis tools via EViews in this chapter. For example, this chapter will go over multiple regression analysis, descriptive analysis, correlation analysis, and the pooled ordinary least squares model as stated in the previous chapter. Then, the findings and the results of the analysis will be interpreted. This chapter will anchor the analysis of the collected data sample. The several analysis tools such as descriptive analysis, multiple regression, correlation analysis pooled ordinary least squares model shall be analysed with the aid of Eviews as discussed in the preceding chapter. Furthermore, the findings and results of the study shall be empirically interpreted in this chapter.

 

 

 

4.2        Descriptive Analysis

	Mean	Median	Maximum	Minimum	Std. Dev.	Skewness	Kurtosis
CPS	0.33	0.30	1.41	0.00	0.24	1.00	4.87
FSZ	8.60	8.51	10.67	7.40	0.56	1.10	4.58
PRO	0.06	0.03	11.13	-0.44	0.49	20.97	471.48
TANG	0.94	0.43	50.34	0.02	4.37	9.12	87.15
GRO	2.20	0.84	523.40	0.00	22.29	23.27	544.67
LQY	5.34	2.09	110.02	0.08	10.59	5.82	45.46
	Jarque-Bera	Probability	Sum	Sum Sq. Dev.		Observations
CPS	172.16	0.00	179.50	31.16		551.00
FSZ	168.63	0.00	4738.85	172.43		551.00
PRO	5079065.00	0.00	32.90	132.45		551.00
TANG	170217.60	0.00	515.29	10510.48		551.00
GRO	6785814.00	0.00	1214.24	273220.00		551.00
LQY	44510.66	0.00	2942.21	61652.74		551.00

Table 4.4.1.1: Descriptive Statistics of Technology Sector

Table 4.4.1.1 above presents the descriptive analysis of the data for this study. from the analysis above, if can be inferred from that there is high volatility amongst PRO, TANG, GRO and LQY because the standard deviation value is greater than the mean value. However, both CPS and FSZ has little volatility. As regards the CPS, it can be inferred from the average value that is lesser than one that that firms in the industrial product sector rely more on equity financing than debt financing.  The total observation is 551 firms. On average, the firms in the Malaysia Industrial product firm fund their business operation with 33% debt financing. in addition to this, an average of 6% of the firms capital structure is being financed by the internal financing as against external financing during the period under investigation. The firms in the industrial product sector consist of large firms.

The asset tangibility, on average, is 94% and their current assets are 5 times more than their current liabilities. This implies that the firms under investigation are capable to fulfilling their short-term obligations. The growth opportunities on average is 2 this implies that the firms are growing at twice more than they are during the period under investigation.

4.3        Overall Analysis

4.3.1        Multicollinearity Test

In a panel data analysis, there is high possibility for two or more variables to be highly correlated. Hence, the need for multicollinearity test. In order to determine whether or not there is multicollinearity between the variables used in this study, variance inflation factors (VIF) and correlation matrix will be adopted.

	Coefficient	Uncentered	Centered
Variable	Variance	VIF	VIF
FSZ	0.000292	246.0422	1.036543
PROF	0.000368	1.019968	1.005061
TANG	4.70E-06	1.065249	1.018550
LQY	7.98E-07	1.272371	1.013982
GRO	1.79E-07	1.015198	1.005352

Note: VIF >4 is used as a threshold value for multicollinearity to exist

Table 4.3.1.1: Variance Inflation Factors (VIF)

Table 4.3.1.1 above presents the values of the variance inflation factors (VIF) values for each variable. The results of the centered VIF revealed that the coefficients of all of the independent variables are lesser than 4 (four). This implies absence of the problem of multicollinearity among between the variables. Meanwhile, a lot of scholars have argued that variance inflation factors (VIF) alone is not enough to determine existence of multicollinearity between the variables. Thus, correlation matrix approach will also be utilized to substantiate the initial findings.

 

 

	DEBT_RATIO	FSZ	PROF	TANG	GRO	LQY
DEBT_RATIO	1.000
FSZ	0.019	1.000
PROF	-0.028	-0.054	1.000
TANG	-0.059	-0.123***	0.046	1.000
GRO	0.032	0.072*	-0.000	-0.004	1.000
LQY	-0.379***	-0.108**	-0.018	-0.024	-0.010	1.000

Note: Correlation coefficient >0.700 is used as a threshold value for multicollinearity to exist. “*” indicates significant at 90% confidence interval in t-test; “***” indicates significant at 99% confidence interval in t-test.

 

The correlation matrix in presented in Table 4.3.1.2 reveals that there is no strong correlation between the variables since the correlation coefficient is less than 0.70. in this regard, it can be concluded that there is absence of multicollinearity problem among the variables. in addition to this, the probability value of the correlation coefficients revealed that there is significant relationship between the explanatory variables and the dependent variable. same is also found among the independent variables.

 

 

 

4.3.2        Estimation

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.503440	0.148064	3.400155	0.0007
FSZ	-0.014830	0.017079	-0.868317	0.3856
PROF	-0.016416	0.019188	-0.855546	0.3926
TANG	-0.003916	0.002168	-1.806053	0.0715
LQY	-0.008654	0.000893	-9.687236	0.0000
GRO	0.000325	0.000423	0.768156	0.4427
R-squared	0.151428
F-statistic	19.45102
Prob(F-statistic)	0.000000

Table 4.3.2.1: Pooled Ordinary Least Squares Model (OLS)

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	1.690992	0.406982	4.154958	0.0000
FSZ	-0.157062	0.047069	-3.336823	0.0009
PROF	-0.002328	0.010055	-0.231505	0.8170
TANG	-0.001724	0.003331	-0.517470	0.6051
LQY	-0.002426	0.000839	-2.892991	0.0040
GRO	0.000126	0.000221	0.571797	0.5677
R-squared	0.841732
F-statistic	25.15153
Prob(F-statistic)	0.000000

Table 4.3.2.2: Fixed Effects Model

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.931535	0.249328	3.736181	0.0002
FSZ	-0.068435	0.028740	-2.381211	0.0176
PROF	-0.001991	0.009996	-0.199180	0.8422
TANG	0.001704	0.002414	0.706034	0.4805
LQY	-0.003549	0.000785	-4.521976	0.0000
GRO	0.000093	0.000218	0.427065	0.6695
R-squared	0.054051
F-statistic	6.228143
Prob(F-statistic)	0.000012

Table 4.3.2.3: Random Effects Model

The estimation results of the pooled ordinary least squares model, fixed effects model, and random effects model are presented in table 4.3.2.1, 4.3.2.2, and 4.3.2.3 above respectively. Based on the assumption that there is the error component is devoid of multicollinearity, constant variance and zero mean, the pooled OLS estimation ignored individually unique effects. However, these assumptions are incorrect since various firms possess different characteristics, which are referred to as heterogeneity. If the existing variations in individual effects are significant, ignoring non-observable 62 heterogeneity may distort the accuracy of estimation. Therefore, pooled ordinary least squares (OLS) model is not the best suit estimation for this study. Random effects model or fixed effects model would be a better estimation.

4.3.3        Hausman Specification Test

In order to determine the best model that should be adopted among the fixed effects or random effects model the Hausman specification test will be carried out. The hypothesis for the test is formulated as thus:

H0: Random effects model is appropriate.

H1: Fixed effects model is appropriate.

The estimation results are shown as table below:

Test Summary		Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Cross-section random		23.426062	5	0.0003

Table 4.3.3.1: Hausman Specification Test

Table 4.3.3.1 above revealed the condensed result of the Hausman specification test, the p-value is 0.0003, which statistically significant 0.05. The decision rule is to reject the null hypothesis if the p-value is lesser than 0.05 and if otherwise, we do not reject the null hypothesis. Since the p-value is 0.0003, the null hypothesis is rejected and fixed effects model will be adopted as the best model for this study. On this basis, it is concluded that there is link between individual effects and the model’s explanatory variables.

4.3.4        Robustness Check

Variables	Pooled OLS Model		Fixed Effects Model		Random Effects Model
	Coefficient	Prob	Coefficient	Prob	Coefficient	Prob
FSZ	-0.014830	0.0009	-0.157062	0.0176	-0.068435	0.0176
PROF	-0.016416	0.8170	-0.002328	0.8422	-0.001991	0.8422
TANG	-0.003916	0.6051	-0.001724	0.4805	0.001704	0.4805
LQY	-0.008654	0.0040	-0.002426	0.0000	-0.003549	0.0000
GRO	0.000325	0.5677	0.000126	0.6695	0.0000933	0.6695
R-squared	0.151428		0.841732		0.054051

Table 4.3.4.1: Overview of the Three Models

Table 4.3.4.1 presents the robustness check of the abridged estimations of the pool OLS Regression, Fixed Effects Model and the random effects model. From the models, it was revealed that the Fixed Effects Model has the highest R-squared value of 0.841732 or 84.17% as compared to pooled OLS regression (0.151428 or 15.14%) and random effects model (0.054051 or 5.40%). The R-squared value explains the percentage of variation of the dependent variable that is explained by the independent variables. For instance, the R-squared value of the Fixed Effect Model is 0.8417. This implies that 84.17% of the variation in debt ratio can be explained by firm size, profitability, tangibility, liquidity, and growth opportunities. This has left 15.83% of the variation of the dependent variable to be explained by other independent variables that are not captured in this model. Relative to R-squared value of the Pooled OLS Regression and Random Effects Model, the Fixed Effects Model is considered the best for this study.

The probability values of the fixed effects estimation revealed that two out of five variables are statistically significant at 90%, 95% and 99%. Firm size and liquidity have the P-value 0.0176 and 0.000 respectively. This implies that these two variables are statistically significant to influence the dependent variable, capital structure. Since the coefficient values of firm (-0.157062) and liquidity (-0.002426) are negative, this implies that these variables are negatively correlated with capital structure. This implies that a unit increase in firm size and liquidity will lead to a 15% and 0.2% decrease in capital structure respectively. On the other hand, profitability, tangibility and growth opportunities are statistically insignificant to influence capital structure.

4.3.5        Findings Interpretation

In summary, the pooled OLS regression is not the best estimation model for this study because it ignores heterogeneity. In this regard, the Hausman Test was carried out to determine the best model between the fixed effects model and random effect model. the fixed effect model was chosen as the best model for this study. Both the Variance Inflation Factor (VIF) and the correlation matrix were chosen as the multicollinearity test and it was verified that there is absence of multicollinearity in the estimation.

In respect to the fixed effects model presented in table 4.3.2.2, firm size and liquidity are the two of the five independent variables that are statistically significant enough to influence the dependent variable at a 99% confidence interval. The other three independent variables that are not significant to influence the dependent variable are profitability, tangibility and growth opportunities because their probability values are greater than 0.05 significance level.

Specifically, the results of the fixed effects model revealed that there exists a negative relationship between firm size and capital structure which implies that large size firms in the industrial product sector of Malaysia are using less debt finance. This is in congruent with the research hypothesis as well as the findings from the empirical studies of Dessalgne (2018), Fisseha (2010), Suarez (2016), Marete (2015) and Corina, RiaMurhadi and Wijaya (2017). However, the finding is in line with the empirical studies of Hussain and Miras (2015), Baloch, Ihsan, Kakakhel, and Sethi, (2016) and Paseda (2016). At the same time, the result did not conform with the theoretical framework presented in Figure 2.1. The findings challenged the trade-off theory that suggests that suggest positive relation with debt ratio because large size firms easily have access to debt finance relative to small size firms.

Furthermore, the fixed effects model revealed that profitability is insignificant to capital structure. The insignificant relationship implies that profitability is not a determinant of capital structure. the negative coefficient is in congruence with the study’s research hypothesis as well as the previous empirical findings of Dessalgne (2018), Awan, Faridi, and Ghazi (2016), Hove and Chidoko (2012), Gharaibeh and AL-Tahat (2020), Ramli and Affandi (2015), Corina, RiaMurhadi, and Wijaya (2017), Marete (2015), Akdal (2011), Thomas, Chenuos, and Biwott (2014), Fisseha (2010), Paseda (2016) and Hussain and Miras (2015). This finding also cast doubt on the research framework and also challenged the trade-off theory. However, the negative coefficient the pecking order theory which postulates that more profitable firms do not have reasons to borrow to cater for their capital expenditures.

In addition to this, the probability value of tangibility as estimated with the fixed effect models revealed that it has no significant influence on the capital structure. This result is in congruence with the study’s research hypothesis as well as the findings of Silva, Cerqueira, and Brandão (2017), Nasution, Siregar, and Panggabean (2017), Hussain and Miras (2015), Fisseha (2010), Akdal (2011), Corina, RiaMurhadi, and Wijaya (2017), Ramli and Affandi (2015), Awan, Faridi, and Ghazi (2016), Hove and Chidoko (2012). However, the research outcome of Baloch, Ihsan, Kakakhel, and Sethi (2016), Acaravci (2015), Dessalgne (2018). However, this finding is supported by the empirical studies of Awan, Faridi, and Ghazi (2016) and Tita (2016). This finding also cast doubts about what is proposed in the theoretical framework and also challenge the agency cost theory because tangible assets means that companies have low leverage which exposes them to financial distress. However, it conforms with the pecking order theory that proposes that tangibility is insignificant to short term debt financing.

In addition to this, liquidity was found to exert significant negative relationship with capital structure in this study. This result supports the study’s research hypothesis and the pecking order theory that suggests that firms will prefer internal funding rather than sourcing for debt financing because high liquid firms will maintain high level of current assets that is easily convertible to cash. This result is also supported by the findings of Hussain and Miras (2015), Thomas, Chenuos, and Biwott (2014), Akdal (2011) and Mbonu and Amahalu (2021). However, the finding is in congruence with the discoveries of Dessalgne (2018) and Corina, RiaMurhadi, and Wijaya (2017). Some more, it supported the theoretical framework.

Lastly, growth opportunities also exert positive but insignificance influence on debt ratio. It refutes the study’s research hypothesis and it is also in congruence with the empirical evidence of Silva, Cerqueira, and Brandão (2017), Paseda (2016), Mbonu and Amahalu (2021) and Fisseha (2010) and Acaravci (2015). The positive coefficient of the variable revealed that firms with high growth opportunity borrows more debt to finance their investment since the internal funding is not relevant with pecking order theory. However, the probability value is insignificant which implies that growth opportunities is not a factor that determines capital structure of firms in the industrial product sector in Malaysia during the period under review. Nonetheless, the insignificant relationship between growth opportunities and debt ratio is consistence with the empirical findings of Hussain and Miras (2015) and Corina, RiaMurhadi, and Wijaya (2017).

In summary, the result of the fixed effects model revealed tangibility, profitability and growth opportunities are not determinants of capital structure of listed companies in Malaysia industrial product sector. The possible reason is that these variables are not the important elements of considerations for investors while investing in the Malaysia stock market. These findings is in congruence with some of the empirical studies that have been carried out in the past. The possible reasons for these discrepancies might be because of different study period as well as differences in the sample characteristics and analysis techniques. In addition, the results findings are said to be primarily applicable to high levered firms in Malaysia since the data sample includes only those companies that finance most of their operations with debt.

4.4        Chapter Summary

This chapter anchored the data analysis and interpretation. The following chapter will summarize the key findings of the study before pointing out the study’s limitation. The recommendations that emerge from the findings will also be made in the next chapter before the conclusion of the chapter. The analysis and interpretation of the findings were completed in this chapter. The next chapter will summarize the key findings, determine the study’s limits, offer recommendations, and make a conclusion.

 

 

5         CHAPTER 5 CONCLUSION AND RECOMMENDATIONS

 

5.1        Introduction of Chapter

Following the data analysis, results and interpretations covered in the preceding chapter of this study, the key findings of the study will be discussed in line with the limitations encountered in this study. The chapter will also discuss the recommendations and implications that emerge as from the findings.

5.2        Summary of Major Findings and Conclusion

The study’s main objective is to identify the determinants that influence capital structure of listed firms in Malaysian Industrial Product sector. the specific objectives of the study is to investigate the influences of firm size, profitability, tangibility, liquidity and growth opportunities on capital structure. to achieve those objectives, the study adopted panel data analysis.

After eliminating the outliers, a total of 92 firms listed in the industrial product were selected for this analysis, having 552 out of 600 observations. The comparison between the Apriori expectation and actual findings of this study will be presented in Table 5.2.1.

Hypothesis	Variables	Expected Results / Relationship	Research Findings
H1	FSZ	positive	negative
H2	TANG	Positive	insignificant
H3	PROF	negative	Insignificant
H4	LQY	Negative	Negative
H5	GRO	negative	insignificant

Table 5.2.1: Summary of Research Hypotheses and Findings

Following the comparison of the expected results with the actual findings of the study as presented in Table 5.2.1 above, it was found that only H4 met the expected result and the hypothesis is accepted. Meanwhile, H1 has an opposing finding compared with the expected result. On the other hand, other hypothesis; H2, H3, and H5 were rejected. In essence, firm size and liquidity were found to be the only determinants of capital structure of firms in the Malaysia industrial product sector during the period under analysis. in congruence with this, profitability, tangibility and growth opportunities are inconsequential as a determinant of capital structure of firms in the Malaysia industrial product sector during the period under analysis. since the data sample includes firms that finance their operation with both debt and equity during the period under analysis, the findings of the study will be applicable to investors and managers of firms in the Malaysia industrial product sector. Although, the vast majority of the actual findings contradict the hypotheses and theoretical framework established, this might be as a result of some missing variables that are not captured in this model or as a result of differences in management practices.

5.3        Limitations

A study of this type cannot be completed without facing at least one or two limitations. To begin with, the study covered the analysis period between 2016 to 2021. This might pose a potential setback on the analysis because a narrow study range might impact the robustness of the result. Some more, the study examined all the firms under the industrial product sector all together, this may not allow for the examination of determinants of capital structure of the various sub-sectors that made up the industrial product sector.

In addition to this, the study included five explanatory variables as the determinants of capital structure. these variables may not be the only true determinants of capital structure because previous studies have included some other variables as the determinants of capital structure. the insignificant influence of this variables in the study may infer that these variables alone are not enough to determine the capital structure of firms in the Malaysian industrial product sector.

5.4        Recommendations

Following the limitations encountered in this study, following recommendations were made:

Firstly, the study recommended that the analysis period should be extended beyond six years in order to capture the effect of the changes over time. in addition to this, future studies should include more explanatory variables that were not included in this study in order to strengthen the model. the influence of other firm specific factors such as risk, age, earnings volatility and non-debt shield tax should be investigated in the future study. In the same vein, future study can investigate country specific factors as a determinant of capital structure. Furthermore, the future study can relax the selection criteria in order to increase the sample size. Additionally, future study should carry out a sub-sectoral analysis of the determinants of capital structure in the industrial product sector.

5.5        Implications

The study’s main contribution intends to offer new knowledge to investors, stakeholders and managers about the determinants of capital structure of firms listed under the Industrial product sector of Malaysia. it offers an insight to managers and owners of companies be aware of the factors that may influence their financing decisions. In addition to this, the findings of this study provide financial managers the needed information that enhance their capital structure and investment decisions in order to efficiently utilize their resources.

The contribution of the findings of this study are not only relevant to the managers or owners of firms in the industrial product sector, it is also important to policymakers in Malaysia and other countries with the same characteristics. It will aid policy makers to formulate business and financial structure strategies and policies that will enable firms to maintain optimal financial leverage among firms in the industrial product sector of Malaysia.

5.6        Chapter summary

Conclusively, this dissertation examined the nexus between five explanatory factors studied and capital structure of firms in the industrial product sector. The study achieved the broad and specific objectives of this study. the limitations of the study were discussed and recommendations were made for future studies.