The study findings indicate that the market debt ratio TMLEV and the debt ratio TLEV seem to be significantly positive and have a negative effect on Return on Assets, Return on Equity an
CHAPTER 1: INTRODUCTION
Research Context
Capital structures have recently been a major emphasis in the financial sector, but their existence and financial relevance have not been thoroughly understood The definition of the structure of capital is highly significant The financial framework reflects the corporation's funding mix It has a significant effect not only on the return of an organization, but also on the sustainability capability in the face of economic shocks, which guarantees its key hand in facilitating the organization fulfill its long-term objectives and goals In addition, with dividend policy and acquisition, it is important for any organization in various sectors to take decisions on the capital structure, since they are closely connected with the goal of optimizing the shareholder value and the willingness of the business to remain competitive on the market
Modigliani and Miller (1958), in the light of perfect market conditions, proposed an obsolete hypothesis of the capital system of firm value In this industry, investors are given equal access to any aspect of financial information and trading costs and taxes for dividends and capital gains are not applicable The real world’s economy, though, is imperfect Consequently, many hypotheses of funding decisions dependent on the realistic scenario have been built from time-to-time to illustrate the function and role of the capital mix in business valuation A few years later, Modigliani and Miller (1963) amended their previous assertion and clarified, which interest costs became tax deductible, which may maximize the valuation of a business by higher debt ratios The literature on capital structure has steadily been rounded out over time Many scholars have identified various variables that influence both financial performance and strategic decisions in finance Organization is expected to settle on capital structures between debt and equity instruments, taking into account the varying costs and benefits relating to these instruments This can cause financial instability and ultimately bankruptcy by making a bad judgment on the proportion of equity and debt (Ahmed Sheikh, 2011)
By comparison, the optimum capital structure helps the organization to prevent its financing risks and to effectively boost the firm 's sales While a broad variety of studies analyze the effects of capital structures on corporate viability (Mendell and Mishra, 2011; Gill and Nahum, 2013; Stierwald, 2010, only a few are able to completely explain their effect on the manufacturing industry's business performance Amid diverse and sometimes conflicting findings, these papers draw a clear inference, which is the relationship between the capital structure and the company's performance, however its essence depends on specific circumstances Manufacturing is a significant sector leading to mature and developing economies' growth Manufacturing firms typically have a substantial volume of resources and use leverage for their business (Michael and Stevie, 2014) Consequently, they appear to be influenced by inefficient management of capital structures that leads to decreased organizational performance, restricting numerous manufacturing firms in gaining their economic objective
The study explores the relationship between financial performance and capital structure This also evaluates the effect of capital structure (leverage) on the financial output of the manufacturers reported on the FTSE100 in the traditionally formed UK The report utilizes panel data based on 30 listed UK manufacturing firms entities on FTSE100 for the six-year period between 2014 and 2019 as well as utilizes approaches such as models of regression, fixed effects (FE) and random effects (RE) regression, the dynamic panel GMM of estimator system for interpretation of data and testing on the hypothesis Though the study takes a variety of methods, both conclusions are appropriate
In the study , three evaluation metrics, namely Tobin Q, return on asset, return on equity can be defined as dependent variables, whereas 2 capital structures, including Market Total Debt and Debt ratio, are regarded as independent variables
In addition, the short-term debt and long-term debt ratios are analyzed in order to define the risk-use actions of studied firms Besides that, the research covers consistent, or in other words, control variables, growth opportunities, tangibility and corporate age and size
This chapter provides a concise overview of research report, covering reasons for study motivations , research questions and suggestions.
Research Motivation
This thesis aims to better understand the relationship between capital structure and business performance Previously, no effort has been conducted to examine the effect of capital structure on the results of the manufacturing sector representatives reported in the London Stock Exchange, the best appraisal scheme of 100 qualified UK firms accounted for approximately 81 percent of the overall market capitalization of FTSE100 The research aims to address this literature gap.
Research Objectives and Questions
This study aims to investigate the value of capital structure and its effect on the profitability of 30 listed manufacturing corporations on UK FTSE100 Therefore, the analysis will help business management make better financing decisions and maximize efficiency through capital structure
To fix underlying problems addressed in the rese, the following research questions are asked
The key question of study is: "How does the capital structure impact FTSE100 components' financial performance?” To answer this question of study, two sub- questions need to be examined:
• What are the key principles about firm’s financial performance and the structure of capital?
• How are financial performance and leverage interpreted by a company?
The research scale
This analysis aims to establish the relationship between the structure of capital and the corporate performance of listed 30 UK manufacturers on FTSE 100 from 2014 to 2019.
Synopsis of Chapters
This paper consists of the following five chapters:
Sector 1: Introduction to this article
This chapter outlines the reasons for choosing the research topic, at the same time addressing research questions and research objectives It also gives a short introduction to the methods of this study
This chapter is divided into two sections: The first section outlines fundamental principles such as capital structure, financial performance of the business and the measurement of these metrics This is accompanied by a review of previous research related to capital structure in the UK In addition, this section discusses recent articles to explore the effect of capital structures on the performance of companies The author then analyze and classify reviewed researches into particular groups depending on their study emphasis or analytical methodology in order to make it easy for them to read the report Finally, the chapter discusses several shortcomings in the literature motivated by this analysis
This chapter explains the data and discusses how they are obtained and the methods utilized to examine the topic's econometrics analysis Also clarified are the reasons for picking such a data selection and econometric model Briefly, in the period 2014-2019, data were obtained from a few reliable sources, including FT.com, Fame or Manufacturing Association in the UK, formally recognized by the United Kingdom General Statistical Office The key methodology approaches used in this research include regression of fixed effects and random effects, OLS and Correlation analysis
This chapter outlines the research methodology Next, the data collection 's technique is explored, accompanied by research hypotheses and finally the approaches used to evaluate the topic of econometrics In addition, these are the reasons for following such a methodological approach
This section discusses findings and empirical results regarding the impact of leveraging on corporate performance by following GMM model, robustness monitoring, a random and fixed-effect regression model, and non-linear interaction between the corporate performance and capital structure In addition , the study findings are presented on the capital structure's impacts on the company's financial performance using the technique
The last chapter outlines the key findings of the study and presents a review focused on the results of the analysis In addition , it gives a conclusion and proposed research limitations.
CHAPTER 2: MAPPING THE CONCEPTUAL FRAMEWORK -
Capital Structure
There is a widespread consensus on the definition and nature of the structure of capital Many researchers argue that the capital structure is a relation to how a company finances its corporate operations with numerous long-term funds instruments including debentures, long-term loans, retained earnings, preference shares, and equity shares (Modugu & Eragbhe, 2015; (Ahmed Sheikh, 2011) More precisely, the capital structure is used as a financial concept representing the porportion of debt-generated capital of an undertaking versus the proportion gained by the company's equity(Bhaduri, 2002)
As regards purpose, the capital structure is used to classify cash flows of the firrm into securities and debt-financed assets used for their investments by companies (Gitman and Zutter, 2012) It can be funded from both internal funding sources such as retained earnings, investments of owners and external financial sources like bonds and credits (Gangeni, 2006)
Nonetheless, the fluctuation in the proportion of external and internal financial sources contributes to the assumption that most companies select the appropriate average debt maturity and the particular kinds of funding instruments for the business to ensure that their finance ratio stays similar to the target capital structure This guarantees the efficiency of firm's productivity and maximizes the intrinsic value of the organization (Ehrhardt & Brigham, 2011) In fact, to maximize the profitability of the firm, the cost of capital should be maintained at the lowest level The optimal capital structure is reached at this stage Parmasivan & Subramanian (2009) is characterized as an optimum capital structure, or composition of debt and equity, which causes the company's valuation to achieve its highest point, while retaining a minimum Weighted Average Cost of Capital (WACC)
In addition, capital structure is also regarded as an important financial leverage for both owners' firm and creditors because a weak collateral judgment may have a significant effect on company performance and sustainability (Quiry and Vernimmen, 2005) In the other hand, the adequate capital structure is expected to optimize the returns of various owners in companies and thus improve the potential of the enterprise to succeed in a competitive environment (Yu and Luu 2012)
The main findings related to the capital structure are explained in this paper Partially, M&M theory can be described and then the principle of trade-offs and pecking order principle, and finally the theory of agency cost is demonstrated
2.1.2.1 Theories of Modigliani and Miller
The founders of economic philosophy (also defined as MM theory) became identified as the Modigliani and Miller They also built up a firm foundation for future research studies on the issue of capital structure Modigliani and Miller released a paper on capital costs and corporate finance in 1958 that has a huge influence on finance fields (Luigi & Sorin, 2009) This idea is accompanied with an organization's financial structure and capital expense in the so-called perfect capital markets A further paper, "Corporate Income Taxes and Capital Cost: a Correction" was published 5 years later The paper is a revised and expanded document that involves a 'tax' key indicator of business profitability
The principles of Modigliani 's Theory and of Miller 's Theory (1958) were originally based on assumptions on an efficient market in which purchasing and selling securities freely without asymmetrical information presence, taxes and inflation and transaction costs (Bose, 2010; Quiry et al., 2009) Additionally, their research assumed that both creditors and corporations had the same rate of borrowing and financing in a perfect capital market
For the "tax free" policy, a corporation could easily merge any aspect of its debt and equity in order to create a capital structure regardless impacting the valuation and efficiency of the firm, because its worth does not rely on its capital structure and other factors of the profitability of the company like cash flows (Ross, 2011) Modigliani and Miller (1958) have believed that, whether the business profit or future opportunities are the same, the leveraged corporation's value (a corporation with a combination of debt and equity) will be equal to an non-leveraged company's value (a company that is completely equity-financed) If an investor owns a leveraged company's stake, it will cost him the same as buying the stock of a non- leveraged company In addition, the asset profitability and risk, as shown by MM theory (1958), plays importance role of the firm, not the capital structure Otherwise speaking, the assumption that the corporation's financial structure does not influence the market value of the corporation indicates that debtors and owners of the corporation have the similar priority (Kouki and Said (2012)
The M&M theory (1958), however, was consequently particularly controversial in the financial sector because of its impractical approach (Villamil, 2007) As a result, Modigliani and Miller proposed a new vision of an ideal financial system in 1963 that modified the previous concept and allowed more practical assumptions In the initiative, the tax benefit will be defined as a controlling capital structure Because taxes are not levied on interest, a corporation which fulfills its tax obligation will benefit from the interest As a result, the weighted average cost of capital (WAAC) could be minimized because of the so-called tax shield (Brigham & Ehrhardt,
2010) Brigham & Ehrhardt (2010) have proposed that where corporate debt levels are low, the tax shield appears to be greater
Nonetheless, once the indebtedness ratio approaches a certain point, the rise in equity expenses overcomes the tax shield reduction due to the cost of debt financial hardship, and the possibility of not fulfilling business debt obligations M&M
(1963) also suggested that, if the corporation requires higher levegage, the return on equity and the financial performance of the business will be increased as interest- bearing payments on loans would be tax free and debts' costs would in most cases be smaller than equity (Asaf 2004)
As a consequence, debt thereby provides an appealing source of finance for businesses, as opposed to equity, as the interest charged on the loan would deduct taxable income and minimize the sum paying for goverment by taxation Nonetheless, debts' costs escalate when leverage rises and credit ratings deteriorate accordingly Most aspects stay the same; gained debt's advantages are bigger with higher tax rates
MM theory assumes that leverage may influence the efficiency and valuation of a business because interest payments are tax deductible In practice, however, there is no such thing as the so-called efficient market because of the existence of many financial considerations including agency costs, transaction costs, asymmetrical information, financial distress costs and taxes The Modigliani and Miller assumptions thus appear to lose much of their power of interpretation (Crabb et al.,
2014) Despite criticism of its unrealistic theories, MM theory is now widely accepted as a solid foundation for other theories related to capital structure
With the MM theory emphasizing the so-called optimum capital structure, Myers'
(1984) proposed theory of trade-off is one of the most influential theories for capital structure that emphasizes the correlation between tax debt reduction and financial distress costs (Oruc, 2009) It noted that, as suggested by MM theory, the benefits of tax shields tend to offset for the financial expenses of the business, including the agency costs and risks of financial distress (Mostafa & Boregowda 2014)
Each source of funding has its own costs and returns related to insolvency risk and the corporation's profitability (Awan and Amin 2014) A corporation with more tax benefits will consider issuing debt to fund its operations, but its risk of default may rise (Chen 2011) As shown by Baxter (1967), capital losses resulting from financial distress might be very costly and might trade off the debt finance's tax advantages This means that debt will offer the company both benefits and drawbacks One of the key benefits is that the business will profit from debt-tax reductions explained in the 1963 M&M principle of irrelevance The earnings of a tax-protected corporation will produce a value-added gain by using leverage In the other hand, if a large volume of leverage is used, the company will suffer the risks resulting from a rise in the risk of bankruptcy Bankruptcy firms typically tend to pay large litigation and accounting costs and have trouble retaining clients and vendors (Awan and Amin 2014) In comparison, bankruptcy also causes businesses to liquidate their properties below their worth (Ehrhardt and Brigham, 2011) It can be shown that the financial issues most commonly arise where a business uses a significant proportion of its capital structure debt
Firm performance
Because of its multifarious meaning, there is no agreement on the business performance's definition Nonetheless, many other researchers recognize that business performance could even operate as a composite tool to assess how well an organiczaction generally shareholders, market and financial performance, performs its main parameters (Glick, Washburn, & Miller, 2005)
Performance measurement is part of performance's operation and finance The company's substitute choices for measuring operational and financial performance based on the objectives it has set for a particular period of time (Tudose, 2012) The evaluation of company performance utilizing key financial ratios should thus be coordinated into the non-financial evaluation According to Abdulmalik et al (
2014), financial performance evaluation such as profitability on assets, profit maximization, and the value of shareholders establish the core of the company's efficiency On the other hand, measurement metrics of operating performance such as sales growth and corporate size extend the business performance's definition since they concentrate on other major elements that might also influence financial performance
The measurement of performance often relies on the information provided in the measuring instrument and the category of measures utilized The traditional indicators for estimating business performance also provide, following to Tudose
(2012), such as the liquidity, ,leverage, receivables turnover, inventory turnover ratio, and return on investment (ROI) In addition to these evaluation metrics, the advanced indicators of value also include:
- Metrics of Accounting includes Return On Equity (ROE), Return On Assets (ROA) or operating profit; earnings per share or net profit;
- Metrics of hybrid (financial and accounting) including Return on Investment (CFROI) and economic value added (EVA);
- Market Metrics: Total Shareholder Return, Market Value Added;
- Financial Metrics: Net Present Value (NPV);
As mentioned above, the performance of the company can be tested in different ways The measurement method of the corporation can depend on how the corporation or its investors want to measure the financial operations
Generally , the company and its investors can evaluate the financial and operational performance of certain divisions or the entire administration (Malm and Roslund,
2013) Nevertheless, this report just targets to measure the financial performance of the whole company in terms of profit growth by using the evaluation instruments commonly shown in previous studies.
Empirical Review
A significant variety of the hypotheses above come to the similar assumption that debt could impact the financial performance and valuation of a business in an imperfect market in many respects
A significant variety of the hypotheses above come to the similar assumption that debt could impact the financial performance and valuation of a business in an imperfect market in many respects Nonetheless, the nature of the relationship between the corporate performance and capital structure remains questionable as study findings contribute to conflicting conclusions on that accomplices Empirical experiments on leverage and company performance may usually be separated into two categories The first group's study papers regard leverage as the dependent variable and aim to identify factors, which include company performance measurements In the 1998-2001 research by Nguyen and Ramachandran (2006) on the capital structure of perhaps 600 Vietnamese small and medium-sized enterprises (SMEs), the phrase 'capital structure' can be identified as the dependent amount of debt and equity that can be utilized to fund a company The key finding seemed to be that almost all businesses in Vietnam depended heavily on short-term bank loans instead of mutual funds as equity markets developed during the research period As regards the factors of capital structure, empirical indicators like company's size and growth revenues are frequently seen in international analytical literature on the Vietnamese industry Nevertheless, a pool of conflicting research accumulates the power of revenue and tangibility Nguyen and Ramachandran (2006 ) find that the company's success has a negative effect on short-term debt The more the business expands, the greater the need for working capital In comparison, their study results have had a tangibility's negative effect on financial leverage
This analysis is structured according to the concept of the second category, which recognizes some factors of company success and discusses leverage as an independent variable capability of optimizing the company's value Nonetheless, among researchers following this path, there is no agreement on the capital structure-company profitability relationship
Some research found a strong link between firm performance and leverage (Ghosh and et.al, 2000; Hadlock and James, 2002) Ross (1977) concluded that a corporation with bad prospects needs to offer fewer debts than one with stronger visions as debt issuance may raise bankruptcy's capacity Moreover, the corporate's valuation will grow with the leverage ratio becauses of an improvement in investor expectations of the current situation of the company
Equally, Berger and Bonaccorsi di Patti (2006) stated that an United States Bank with a higher- debt ratio has better business performance that is reflected by efficiency of profit In specific, a debt ratio rise of 1% unit would result in a profitability increase of 6% unit They also argued that raising more debt will lower the agency's funding costs and enable management to behave further in the shareholders' expectations, ultimately enhancing the valuation of the company Abor (2005) reported that capital structure substitutes (i.e., short-term debt to total assets and gross debt to net assets) have a substantially positive effect on equity returns In his research , two econometric models called regression and correlations were used to elucidate the 1998-2002 relationship between capital structure and profitability of listed firms on the Ghana Stock Exchange Using the same analytical methodology as Abor (2005), research conducted by Gill, Biger and Mathur (2011) also found a strongly positive relationship between capital structure and firm results
On the other hand, there is a negative correlation between debt and financial performance (Nawaz Ahmad and Mohsin, 2016; Tian and Zeitun, 2007; Abeywardhana, 2016) Research on the relationship between capital structure and firm performance (ROA, ROCE) by Dilrukshi Krishanthi Yapa Abeywardhana
(2016) has shown that leverage and liquidity are negatively associated with corporate performance Research results also found a strongly positive correlation between size and corporate performance
Besides that, Tian and Zeitun (2007) conducted a research on 167 Jordanian firms between 1989 and 2003 and the introduction of the model of Random Effect (RE), capital structure had impacted negatively on both accounting and financial performance of market Business performance of a company was measured only by market value to book value, market value of equity to book value of equity whereas ROE and ROA were used to calculate accounting performance They concluded that businesses with a high debt ratio typically have poor company's performance owing to underrating of bankruptcy costs, that may cause companies to incur more debts than they should A research by Eriotis, Franguoli & Neokosmides (2002) evaluating several various segments in Hong Kong has revealed an inverse correlation between firm's profitability and capital structure
Furthermore, a majority of studies proposed a non-linear relationship between capital structure and corporate performance It means both variables can have negative and positive correlation Stulz (1990) suggested a model where debt may have beneficial impacts on company financial performance Positively, loan settlement allows management to carry out the business liability duty, reducing overinvestment problems The negative impact is that debt payment might drain company cash flow or profitability assets
Subsequently, in most organisations, capital structure is considered closely linked to financial results and substantially sensitive to financial performance percentages Conversely, there is no agreement on the relationship between firm performance and capital structure (leverage) that needs more research This research project offers empirical evidence of current capital structure hypotheses and fills the literature void as described above.
CHAPTER DATA AND METHODOLOGY
Data
This study's main objective is to examine with empirical evidence of capital structure's effects on the financial performance of listed manufacturing businesses in FTSE 100 The certain section outlines the technique of data collection and discusses assumptions, parameters, statistical analysis and econometric models to investigate autocorrelations across multicollinearity errors
At first, the thesis collects secondary data from evaluating such journal database and academic analysis include Social Science Research Network (SSRN), the British Library, UWE Online Library, Google Scholar to stimulate a dialogue on capital structure theories These widespread and realistic scientific papers will make a contribution to the literature review of the research, laying a solid basis for methodological approach
Second, data is collected from manufacturing listed companies’ financial reports between 2014 and 2019 in United Kingdom and financial sources including FAME or DataStream The figure for capital structure ratios like profitability measurements (e.g Return on Equity), etc are obtained from the financial databases of businesses All databases are also applied to measure control variables (corporate size, tangible assets, growth) Lastly , the panel data format are applied for the research to take advantage of the selected estimators whereas preventing a growth in the observations' quantity or degrees of freedom, thereby improving estimators' performance
The sample of the whole analysis contains of Thirty listed manufacturing firms on FTSE100 in the six-year period 2014-2019 this is thirty companies possess London
's largest stock exchange market capitalization The sample of the whole analysis contains of Thirty listed manufacturing firms on FTSE100 in the six-year period 2014-2019 Such Thirty companies possess London 's largest stock exchange market capitalization The purpose that these 30 corporations are selected in the London Stock Exchange Market due to their largest market capitalization, thus these firms can be seen as representatives for the developed economy Furthermore, industrial firms are big asset owners that need substantial sums of capital (both debt and equity) to fund their activities and development therefore the debt ratio may be raised in the capital structure to utilize the tax benefit (Kelvin, 2015) Finally, this data sample collection will classify and explain the correlation between corporate performance and capital structure.
Research Hypothesis
This research aims to address the research query by introducing multiple models,
"How does capital structure affect the corporate performances with listed manufacturing corporations of United Kingdom FTSE100 between 2014 and 2019?” In addressing this query, this analysis concentrates on exploring different components of both capital structure and company performances The pecking order hypothesis, as stated in the literature review, indicates that profitable firms would offer smaller debt than losing businesses when they possess more retained earnings to fund certain ventures Several academic papers available to support the pecking order theory often indicate that profitable firms retain less financial leverage since they are capable of funding themselves based on internal sources of finance
The pecking order hypothesis, as stated in the literature review, indicates that profitable firms would offer smaller debt than losing businesses when they possess more retained earnings to fund certain ventures Several academic papers available to support the pecking order theory often indicate that profitable firms retain less financial leverage since they are capable of funding themselves based on internal sources of finance
H1a: There is a negative correlation between manufacturing firms ' capital structure and its performance in the UK 2014-2019 period
On the contrary, the static trade-off hypothesis proposes an opposite perspective that means firms with increased profitability prefer to recruit more debt resulting in high debt level and reduced risk of bankruptcy Indeed, in accordance with the trade-off principle, corporations are entitled to raise debt to take advantage of payments of tax-deductible interest (Modigliani & Miller, 1963)
H1b: Capital structure may have a positive impact on 30 manufacturing companies' performance in United Kingdom from 2014 to 2019
Moreover, indicators that influence indirectly on corporate performance by capital structure such as company age, tangibility and corporate size will be analyzed According to Sửderbom and Teal (2002) research indicates that larger companies are able to optimize their market strength Thus, they will take advantages of their competitive advantages rather than other smaller businesses, which in turn have a positive effect on profitability
H2: The 30 UK manufacturing corporations within their business' sizes affects positively on their corporate performance between 2014 and 2019
Stinchcombe (1965) argues that older businesses have a lot of business expertise in order to eliminate and reduce the possibility of shifting the market
H3: The age of the business and the performance of manufacturing companies in
UK show a positive relationship in the timeframe 2014-2019
Finally, Tangibility is seen as a key aspect impacting corporate performance Firms that account for a major tangible assets' proportion are more prefer leverage so they can mitigate the risk of financial distress result of high intangible assets (Akintoye,
H4: The assess tangibility of the businesses and 30 manufacturing firms' financial performance in UK from 2012 to 2017 have a positive relationship.
Methodology
Figure 1 Categorization of variables 3.3.1.1 Dependent variables
Researchers use various metrics in literature to assess the success of organizations
For example, the company's balance sheet and income statement steps, such as
Gross Profit Margin, Return on Equity and Return on Assets (2012), Salim and
Yadav (2013), Cole and Lin (2000), Majumdar and Chhibber (1999) carried out accounting measurement from the company's balance sheet and income statement, such as Gross Profit Margin, Return on Equity and Return on Assets Meanwhile, other organizational success metrics such as Tobin's Q were used by Zeun and Tian
(2007), Tianyu (2013), Ebaid (2009) Therefore, this analysis is consistent with current research and includes three accounting-based metrics to assess financial performance, including Return on Asset (ROA), Return on Equity (ROE) and
The first performance indicator is Equity Return, a metric of determining shareholder's investment Tezel and McManus (2003) stated that ROE reflects the efficiency of management in producing value for shareholders of companies It is also an important profitability metric that shows the amount of return generated from the shareholder fund investment Equity return is considered the most important metric adopted by researchers because of its collection of financial data derived from business's income statements and balance sheets Furthermore, investors use ROE to determine which businesses have a higher and more sustainable growth in their shareholder's equity returns They can then make a valid financial decision on their investments Return on equity is measured by net income (Loss), split from the balance sheet total book value (Ebrati et.al 2013, Bistrova, Lace, and Peleckiene 2011, Phillips and SIPAHIOGLU 2004) Equity returns are measured by net income Equity return is equivalent to net income divided by total equity
ROE= Net Income/ Total Equity
The second performance metric – Return on Assets (ROA), calculates the profits of businesses derived from the company's total assets In addition, the company's productivity, profitability and success are not considered to be a good insider management ratio but also a common performance measure for many recent studies (Thomsen, Pedersen and Kvist, 2006; Finch and Shivadasani, 2006) Return on assets can be determined by dividing net sales by total assets
ROA = Net Income / Total Asset
The higher the return on Asset ratio is, the greater the reward it gains as the company's earnings are higher than their investments (Westerfield et al 2005) Therefore, Return on Asset can be raised by improving the effectiveness of existing assets or by supplementing the net income of the company (Casteuble, 1997)
Finally, the Tobin's Q performance proxy (1999) is also involved in this paper It acts as a measure of corporate assets on par with market value of a company Tobin's Q Ratio is, in accordance with Chung and Pruitt (1994), an acceptable financial efficiency test to analyze the performance of the company and was carried out by many researchers, such as Hossain et al (2001), Reddy et al (2008), Loderer and Peyer (2002) Tobin's Q is determined by dividing the company's market value by its gross assets
Tobin Q ratio = Firm market value/ Total Asset
Leverage functions as credible source of financing for the company's assets (Brounen, De Jong and Koedijk, 2005) and being applied in a number of research papers, such as Masnoon and Anwar 2012, Mahmoodi 2011, Abor 2007, Kyereboah and Coleman, 2007, Akhtar and Fattouh 2005, Scaramozzino and Harris
2005) In these reports, the financial leverage metrics are determined by the total debt of a company divided by its total assets, the short-term debt of a company divided by its total assets and the long-term debt of a company divided by its total assets In addition, this analysis takes account of a total market leverage, which computes a firm’s level of debt-to-its existing market value This ratio is considered a better measure of solvency in a business, provided that market values are more significant than book values This research, therefore, considers that capital structure (leverage) and total market leverage are independent variables, in keeping with previous studies designed to examine the effect of capital structure on financial performance
This thesis also includes additional variables in econometric models that are likely to impact organizational output after defining both dependent and independent variables The research findings will thus avoid a gross overstatement of the effect between capital structure and corporate success
Taking the Abor (2007), Kyereboah and Coleman (2007), Krivogorsky et al (2009), Jermais (2008) and Tanveer and Sajid (2012) into account, these company characteristics like their size and growth chances, may have an influence on their performance These variables are also included in this research model and act as control variables in order to tackle the statistical bias problems Furthermore, other control variables, namely asset tangibility and company age, will also be introduced into this research model
Firstly, growth prospects are apparent to a company's financial results According to Azarbaijani et al (2011), growth opportunities symbolize mostly the company's success aspirations and therefore show a positive relationship between the survival of a company and its development
The growth potential could be calculated by measuring the percentage change in revenue for several researchers such as Margaritis and Psillaki 2010 and Cassar and Holmes 2003 There is an alternative way to measure company growth, i.e total asset growth, as indicated by the annual shift in the percentage of company assets (Manawaduge et al 2011; Degryse, Goeij and Kappert 2010;); This paper used the average percentage change in the asset of the company or asset growth to measure the growth of the company
Secondly, several research papers indicated that corporate size is a significant determinant of financial success Earlier research studies on bankruptcy models showed that even though the numerical value of their financial ratios is equal, bigger businesses tend to be solvent than smaller ones (Yu and Luu, 2012) This means that in times of recession smaller businesses are more likely to struggle than larger ones (Mitchell 1994) Frank and Goyal (2003) also note that larger businesses are more regulated and have a higher level of risk tolerance Their risk of bankruptcy may therefore be minimized Furthermore, as large firms typically have a higher debt than small firms (Mitchell, 1994), their diversification and low borrowing cost benefits are expected to lead to their financial profitability For these purposes, business size may be regarded as a possible impact on the success of a company
For measuring company size, some researchers (Fosu, 2013) take into account the normal logarithm of the total assets Additionally, some studies (Gonzalez, 2010; Jong et al., 2008, Frank and Goya, 2003) consider the key predictor of company sizing to be the normal total revenue logarithm This analysis uses a total revenue logarithm for measuring company size as total assets are the leverage denominator
To prevent a false relationship in the regression (to prevent any misrepresentation), total sales are the denominator of the scale
Several research by Batra (1999), Lumpkin and Dess (1999) demonstrated the impact on financial results of a company's age in literature Kakani, Saha and Reddy
(2001) claimed that large, typically more experienced companies with learning advantages could produce results In addition, older businesses are likely to benefit from their brand names and prestige that may contribute to a rise in the percentage of revenues The company age is determined based on the number of years since the start of this study
CHAPTER 4: ANALYSIS AND FINDING
Descriptive Statistics
The descriptive statistics are indicated on variables in the 2014 –2019 period of 30 UK manufacturers listed in FTSE100 Six groups are identified as the mean, standard deviation, minimum and maximum values of the main variables examined
Table 2 Description statistics for the 2014-2019 period
As shown in Table 2, 11 variables can be classified differently, dependent, independent and control variables A total of 180 observations are found for each variable in this study
The mean of the Return on Equity (ROE), the Return on Assets (ROA) for dependent variables are 0.183 and 0.069 respectively These figures show that 30
UK manufacturing firms reported on the FTSE100 obtain significant profitability Furthermore, the market performance metric (Tobin's Q) shows a high proportion of mean at 1,223 in comparison with other accounting metrics (ROE, ROA) It means that the UK manufacturing companies ' market values reported on the FTSE100 have substantially higher values than their book values Following to the research of Bond et al (2004), where the market value ratio of an asset of a firm (Tobin's Q ratio) is above 1, the company is expected to be profitable in the future However, the figure may also demonstrate that the organization has indeed been overestimated In comparison, the minimum values of ROE, ROA and Tobin's Q reach -1.02, -0.19 and 0.119, and the maximum values are 0.4 and 1.08 or 6.582, respectively for ROE , ROA and Tobin's Q
The descriptive statistics display that 30 UK manufacturing firms examined are deemed extremely to be significantly levered The average ratio of total-debt-to- total assets is 0,609 that equals to 60 per cent of the studied firms' total assets are funded by debt In order to evaluate the debt-use conduct of these firms, however, the analysis explores the short-term and long-term debt proportions In general, the short-term and long-term debt's mean estimates at 0.223 and 0.38, respectively, reflecting their propensity to issue longer-term debt rather than short-term debt In addition, as seen in the table, the overall leverage of these manufacturing companies does not significantly fluctuate The estimated standard deviation of total leverage is 0.1767 with the minimum value and the maximum value are 0.1231 and 5.729 respectively, whereas the market total debt ratio has a significantly higher mean of 0.89 with minimum and maximum values of 0.04 and 0.82
Besides, the findings in Table 2 showed that asset tangibility has an incredibly large mean value of 0,796, meaning, 79.6 percent of the total asset is expressed by fixed assets on average The chosen companies increase at an average rate of 0.08 with the smallest and largest rates of growth indicate at -0.16 and 2.55 The negative minimum value means that certain FTSE100 manufacturing companies have a negative growth rate in the period of time examined A further element to be observed is business's age
In particular, the average age of the analyzed firms is about 78.5, suggesting that these 30 manufacturers have long-developed performance In this analysis, the sizes of 30 companies also constitute a significant control variable The mean for the organizational size reaches 7.02, whereas the minimum and maximum values are 5.83 and 8.47
The next section consists of an overview of the relationship among variables between 2014 and 2019 to achieve a deeper understanding of the research sample
In addition, the correlation matrix method can be implemented to assess the variables' significance levels.
Correlation Coefficient Matrix
The correlation in statistics consists of the study of variables for the measurement of the statistical correlation between two bivariates The coefficient of correlations is useful since it might suggest a predictive correlation which may be used in practice The correlation values differ from +1 to -1 If the correlation coefficient estimates span a spectrum of +1 to -1, it will be inferred that two variables achieve a perfect degree of association Alternatively, if the value of the correlation coefficient goes to 0, the association of Two variables are considered further fragile
As can be seen the table 3, it represents the correlation matrix's findings for this research for 30 UK manufacturing firms listed on FTSE 100 between 2014 and
Table 3: Correlation coefficients of capital structure assessment metrics and corporate performance
Note: ROE: corporate net income ratio / total equity's book value; ; ROA: corporate net income's financial ratio / total asset's book value; Tobin Q: the ratio of the firm's market value/firm's book value; TLEV: the ratio of firm's total debt/total asset's book value; MTLEV: total debt/ total assets' market value; SIZE: business's asset magnitude; TAN: tangibility (fixed assets / total assets assessed);
GRO: the rate of growth determined by the proportion adjustment over the year in total assets Age measured according to the number of years since the formation
This section indicates the leverage ratios' correlation coefficient regarded as capital structure variables is extremely high In particular, the coefficient of correlation between the MTLEV and the TLEV is about 0.7414 Thus, this analysis explores each debt ratio's impacts has on company's financial performance separately, rather than examining TLEV and MTLEV in the similar regression, in order to deteriorate the multi-linearity issue resulting from the regression model Among the other variables described on table 3, the correlation coefficients are reasonably low
(under 0.5) suggesting their suitability for regression analysis
More precisely, proxies for capital structures with a book total debt ratio (TLEV) and a market total debt ratio (MTLEV) have been expected to have negative correlations with ROE, ROA and Tobin Q, as these variables have a negative correlation coefficient at the level of 10 percent In specific cases the correlation of coefficients among ROE, ROA, Tobin Q and TLEV are -0.0931, -0.292 and -0.448, whereas the MTLEV figures are -0.3421, -0.591 and -0.6752, respectively Moreover, the findings table show that financial performance parameters (ROE, ROA and Tobin Q) as well as capital structure proxies encourage a strong correlation with control variables There is a positive correlation between financial performance variables and growth prospects and company's age at significant level of 10%, although having negatively correlated with asset tangibility and company's size which reached at 10% The independent variables, on the other hand, indicate the opposite pattern TLEV and MLEV contribute positively to the size of the organization, which is 10 percent level Such positive relationship means that bigger businesses are able to issue more debts than smaller ones to carry on exploring their business operation.
Pooled OLS regression
The researcher utilizes the regression of the OLS to find out the relationship between capital structure metrics and leverage variables of 30 analysis of compounds As dependent variable, ROA, ROE and Tobin's Q are related to the results of the corporate profitability equation in table 4 Although Tobin's Q is designed to monitor companies ' market performance, the following two variables are structured to capture accounting performance
Table 4: Pooled regression of OLS - The effect on corporate performance by the capital structure
The findings calculated by the pooled OLS estimators are recorded in this table in efforts to support the author make conclusions about the relationship between corporate performance (CP) and capital structure (CS) The statistics are taken from the 2014-2019 annual reports' data analyses Total debt impact of total assets' book value (TLEV) and total debt of Total Assets' Market Value (MTLEV) at Return on Assets (ROA) is analyzed in the first two columns respectively Column 3 and 4 have appropriate evidence for the estimation of the effects of total debt to total assets' book value (TLEV) and total debt to total assets' market value (MTLEV) on ROE Columns 5 and 6 discuss the impact of the total debt on total assets' book value (TLEV) and total debt to total assets' market value (MTLEV) on Tobin Q Four control variables are included: corporate age (AGE), corporate size (SIZE), business tangibility (TAN), business growth (GRO)
Moreover, the regression of OLS results in all models indicating that the capital structure variable can impact negatively on coefficient correlation with the business performance variable when the leverage ratio estimators are indicated negatively at
5 % level, thus helping to explain the ROA
Moreover, the regression of OLS results in all models indicating that the capital structure variable can impact negatively on correlation with the business performance variable when the leverage ratio estimators are considerably negatively at 5 % level, thus helping to understand the ROA More particularly, the correlation coefficient between ROA and TLEV (book total debt ratio) as well as ROA and MTLEV (market total debt ratio) are -0.099 and -0.040, respectively in columns 1 and 2 These estimates indicate that, if the total debt ratio increases by 1%, ROA would decline by about 0.099% Additionally , if MTLEV rises by 1 percent, a 0.040 percent decrease in ROA will be assumed, while other variables will remain stable ROE has the same phenomenon, with the debt correlation coefficients in Columns 3 and 4 being approximately 0.129 for TLEV and -0.058 for MTLEV, meaning that if the total debt ratio rises by 1 percent, the ROE is expected to decline by 0.13 percent, all else being kept equivalent
The coefficient of correlation for total debt ratio and the regression of Tobin Q indicates at approximately -2.15 This result is substantially higher than the coefficient of MTLEV (-0.653) and other ROA and ROE regression models, meaning that the Tobin Q can be highly impacted by book debt ratio (TLEV) greater than that of ROA and ROE
Moreover, one interesting thought is that the OLS model regression's Adj R – Squared is approximately 0.2419 to 0.5946 It shows that these models can substantially reflect the actual effect of independent variables on dependent variables from 24 to 59 percent Furthermore, the overall f-tests with p-values of the (1) to (6) regression model are all below 1 percent, suggesting the suitability of the model Nonetheless, as indicated in the research methodology, coefficients' consistent and efficient analysis of panel data through using pooled OLS techniques are usually called into question for analysis's panel data as they cannot handle completely the unnoticed effects or individual effects that most scientific studies often use panel data (Baltagi, 2005) FE and RE models are, therefore, handled along with pooled OLS to resolve the unobserved heterogeneity.
Random and fixed effect regression
This section presents the results of the regression models of fixed effects and random effects (Table 5) to analyze individual effects across the variables which have not been observed The results of both RE and FE models show that there are negative correlation coefficients between financial performance of company and those two leverage ratios, but the degree of significance differs slightly Several experiments are performed in this study to select the more effective model between random effects and fixed effects The researcher performs an F-test for the FE- Model, Breusch-Pagan analyzes with the Random Effects and the Hausman test for which method is better between the FE Model and the RE Model In particular, statistics from the F-test indicate that the Fixed Effect model offers more benefits in terms of description of the effect among variables than the model of OLS regression does Additionally, the p-value for the Breusch-Pagan measures and Wald Chi-Squared measures are less than 0.05, indicating that the null hypothesis is dismissed and the Random Effect model is more acceptable for all regressions rather than pooled OLS The Hausman test suggest that the findings of the Chi- square (Prob > Chi2 is less than 0.05) statistics, while the FE model analyzes the relationship between the capital structure and business performance, is consistent and superior over the RE model For most of these purposes, the FE plays a key estimator for the influence of leverage on the company's performance
Especially, the model Fixed Effects' results indicate the negative characterization of the relationship between capital structure and corporate profitability Columns 1-4 demonstrate statistical findings allowing the relationship between capital structures and ROA to be evaluated while columns 5-8 display the regression results with the ROE as a dependent variable, while columns 9-11 iillustrate that the results of Tobin Q regression In general, if the total book debt rises by 1%, there will be a decline by 0.8596% in ROE, 0.357% in ROA, and 1.58% in Tobin Q Likewise, if the MLTEV rises by 1%, ROE, ROA and Tobin Q will decline by 0.0693%, 0.17% and 1.799% respectively, with all other component stable
While the Fixed Effect model can deal with unobserved individual elements in the analysis of the relationship between financial performance of companies and the variables in capital structure, other possible issues such as autocorrelation and heteroscedasticity may still contribute to the inefficiency of coefficient model As a result , the researcher apply the Wald test for heterozedasticity and the Wooldridge test to deal with issues of autocorrelation and heterozedasticity
Table 5: Regression of RE and FE — Capital structure impacts on corporate performance
Table below represents the outcomes that allow the author to analyze the relationships between the corporate performance (CP) and capital structure (CS) calculated by return on equity (ROE), return on assets (ROA) and Tobin Q Statistics was compiled from 2014 to 2019 annual reports' data analysis there are four major control variables: corporate age (AGE), corporate size (SIZE), business tangibility (TAN), business growth (GRO)
Based on the research of Drukker (2003), in the presence of a short period of research, the Wooldridge test cannot be accurated exactly The panel data represent
6 years in this study, from 2014 to 2019 so that the problem of autocorrelation can still be ignored The Wooldridge test is, however, still a reference The fact that the Wooldridge test generates the similar p-value of 0.0000 throughout all six models demonstrate that autocorrelation occurs on all regressions during the research timescale In addition, the Wald test's results for the Fixed Effect model (all Prob > Chi2 = 0.000) suggest the appearance of heteroscedasticity problem In order to manage these problems, the Fixed Effect model is used with a robust standard error process
Table 6 reveals the product of the correlation between the company performance and capital structure calculated with a modified standard error by the FE Estimator
In general, all findings are similar direction as the FE model that shows those 30
UK manufacturers listed on FTSE 100 between 2014 and 2019 having a negative correlation coefficient between the business performance and the leverage In particular, almost each coefficient among proxies of capital structures are stated to be negative and statistically remarkable at 1%, with the exception of the book debt ratio's coefficient in Return of Equity regression which is negative but insignificant This means that an increase in total market debt ratio and total debt ratios, can be followed by a decrease in company's performance Moreover, the findings show that the leverage ratio's influences on Tobin Q is more profound than its influence on the other two profitability ratios, as the correlation coefficients of MTLEV and TLEV in Tobin Q regression are significantly higher than those in ROE and ROA regressions reached -0,136 and -1,583, respectively
Furthermore, in all regressions the R-squared values are very great, increasing between 0.128 and 0.602 In specific, these estimates in ROA models are at approximately 0,602(Tobin Q at 0,218 and ROE at 0,155), which means up to 60 percent of the adjustment in ROA through 30 researched companies could be explained by this model Furthermore, the f-test results show that the models are appropriate
Table 6: Robust standard error with Fixed effect estimator — the effects of capital structure on corporate performance
The results of this table allow the researchers to conclude the relationship between capital structure (CS) and corporate performance (CP) The statistics are taken from the 2014-
2019 annual reports' data analyses Total debt impact of total book value assets (TLEV) and total debt to Total asset's Market Value (MTLEV) at Return on Assets (ROA) is analyzed in the first two columns respectively Total debt effects to TLEV and total debt to total assets' market value (MTLEV) on return on equity (ROE) are measured by columns
6 and 8 The impacts of total debt on total assets' book value (TLEV) and total debt to total assets' market value on Tobin Q are discussed in Columns 10 and 12 The four control variables consist of sale growth (GRO), tangibility (Tan), firm size (SIZE), firm age (AGE).
Generalized method of moments (GMM) estimator
The robust standard error test with FE regression model is considered able to monitor unobserved outcomes in conjunction with heteroscedasticity, but the endogenous issue leading to biased and unreliable estimators still exists (Wintoki, Linck and Netter, 2012) The existence of such an issue may be demonstrated because the simultaneous reverse relation between the capital structure and business results cannot be established For instance, corporate performance often has a major influence on decisions on capital structures In order to enhance the results of the study, Arellano and Bond (1991)'s dynamic GMM panel with a modified standard error is implemented to resolve and improve the estimators' efficiency The results of the GMM method are shown in Table 7
The results of the GMM model reaffirm a negative relationship, as shown by the table, between the business performance and capital structure with 9 measures
Table 7: Capital structure impact on corporate results – two-step GMM estimator system with robust standard error
In this table, outcomes are recorded to allow the researcher to make conclusions on the relationships between the corporate performance and capital structure (CS) that are evaluated by pooled OLS estimators Statistics was compiled in the 2014–2019 period from annual reports' data analyses Total the influences of total debt on the total assets' book value (TLEV) with total debt on total assets' market value (MTLEV) on the ROA The influences of total debt on total assets' book value (TLEV), and total debt to total assets' market value (MTLEV) at return on equity (ROE) are appropriately data given for Columns 3 and 4 Columns 5 and 6 address the impact of TLEV and MTLEV on Tobin Q There is a total of four control variables: sale growth (GRO), tangibility (Tan), firm size (SIZE), firm age (AGE)
This coefficient of negative correlation is statistically significant in 5 % and 10% in the main models, with the exception of MLEV ratios in the equation of ROE as well as the coefficients in the Tobin Q equation for total debt ratios Furthermore, findings show that the control variables' indications, which consist of corporate size and age, tangibility, growth opportunities are associated with all approaches adopted but the degree of interpretation varies slightly
While AR(1) and AR(2) in the first differentiated equation check serial correlation disturbances of first order and second order, Hansen J monitors the identifying limitations of the statistical data As can be seen on the table 7, each p-value of the AR(2) tests can be estimated as higher than 0.10, which indicates that the hypothesis of no second-order serial correlation is not rejectable Similarly, the findings of the Hansen J tests show that the invalidity of system variables hypothesis cannot be dismissed.
Summarize of the finding
In general, it is reported that the essence of the correlation between business performance and capital structure in all models, including in RE, FE and OLS models, is negative and significant In fact, the correlation coefficient between the variables of the capital structure and Tobin is known to be deeper than others The consistency of the results indicates the robustness of the findings Significantly, the degree of impact of the sector 's aggregate debt ratio is considerably greater than the total book market in all three statistics, namely ROE, ROA and Tobin Q This statement is further addressed in the next chapter
With regard to control variables, control variables like tangibility business size, business age and growth opportunities have significantly coefficients in some regression analysis Indeed, there are positive relationship of the estimated coefficients, including the business size and corporate age and growth oppportunities that are statistically significant This indicates that manufacturing firm with greater growth prospects and the profitability ratio will boost their financial performance as calculated by ROE , ROA and Tobin Q The result of this study is in accordance again with previous researches by Tian and Zeitun (2007);
Gleason, Mathur and Margaritis and Psillaki (2010), Jiraporn and Liu (2008) These studies concluded that businesses with high rates of growth will obtain more value and profit from investment funds
On the contrary, the tangibility variable in virtually all models has a negative relationship with business performance, as its coefficients are substantially negative at 5%, however, insignificant in equations of Tobin Q and ROE The findings of this study represent that firms with a high degree of asset tangibility face lower risk of bankruptcy and are more agile when deciding the financial investment to improve the financial capacity of the company The results presented in Table 7 demonstrate that the existence of a non-linear relationship could also be observed when mesuring performance by return on equity tests and the total leverage ratio analyses the capital structure
4.5.1 Non-linear relationship between capital structure and corporate performance
The Non-linear relationship test is often carried out to analyze the non-linear relationship between corporate performance and capital structure, usually to prove a hypothesis that those relationship are negative
This test aims to evaluate the nonlinear relationship between the company's performance and capital structure of 30 UK listed manufacturing firms on FTSE
100 between 2014 and 2019 This quadratic function has been designed and evolved through the research of Berger, Bonaccorsi di Patti (2006) and Margaritis and Psillaki (2010) The results presented in Table 7 demonstrate that the existence of a non-linear relationship can also be observed when assessing performance by return on equity tests and the total leverage ratio analyses the capital structure
Table 8: Evaluating non-linear relationships between capital structure and financial performance of businesses
This table shows the results of a study of non-linear relationships between the corporate performance (CP) and the capital structure (CS), which have been calculated with adjusted standard error by Fixed Effect (FE) estimators The statistics are relied on the 2014-2019 annual data Columns 1 and 2, respectively, analyzed non-linear influences of total debt ratio (TLEV) results and the market total leverage (MTLEV) on the ROA The non-linear influences of the TLEV and the total market leverage (MTLEV), respectively, on return on equity (ROE) were analyzed on columns (3) and (4) The non-linear impact (TLEV) and Overall market leverage (MTLEV) on Tobin
Q were tested on columns (5) and (6), respectively Four control variables include age of business (AGE), size of the business (SIZE), tangibility (TAN) and sales growth (GRO)
More precisely, there are positive correlation coefficient of the total leverage ratio in the equation of ROE that is statistically significant at 1,2106, while the TLEV- ROE relationship switches from positive to negative at the level of TLEV2 (at - 1,661) However, in both ROA and Tobin Q regressions, these correlation coefficients of TLEV and TLEV2 are insignificant It shows that the total leverage ratio is even at a high level correlated negatively with ROA and Tobin Q In addition, at low levels, an increase in the debt ratio would result in an increase in ROE by increasing the financial leverage of the organization Nevertheless, an increase in the debt ratio will reduce the ROA Consequently, at a high debt level, ROE may be decreased as the reduction in ROA overwhelms the rise in financial leverage
It can be summarized that the business performance of 30 UK listed manufacturing firms in FTSE 100 from 2014 to 2019 are impacted negatively by capital structure proxies Nevertheless, in the case of ROE in particular, there is just a utilization financial leverage to create a nonlinear relationship between ROE and the capital structure.
CHAPTER 5: CONCLUSION, DISCUSSION & LIMITATIONS
Conclusion and Discussion
The capital structure has prompted a great deal of discussion among financial sector specialists and has been constantly developing since Modigliani and Miller first introduced in 1958 Their classical model suggests that the capital structure is unrelated to the value and financial performance of a company Nevertheless, as Modigliani and Miller (1958) stated that their proposition is only meaningful in an efficient market (a market where no taxes, bankruptcy and transactions exist), the theory of debt irrelevance is barely rational After five years, Modigliani and Miller
(1963) started a new proposal for a capital structure that altered their tax deduction scenario and developed a theory that included tax benefits of debt Some of the theories of capital structure later emerged, such as the pecking order, the trade-off theory and the agency theory, and sought to confirm the amount of leverage through cost-benefit analysis Some empirical studies (such as Tian and Zeitun 2007; Yazhu
F and Yanfei 2015) are followed by the theoretical framework described above with the objective to investigate the impact of the capital structure, in particular the financial performance of the company However, the correlation between the capital structure and the company performance of UK manufacturing firms is a gap in the literature
This study aims to make a great contribution to the current literature by identifying the correlation of capital structure with the financial performance of
30 UK manufacturing companies listed in FTSE100 between 2014 and 2019 The research variables are classified into three parts: dependent, independent and control The various capital structure proxy measurement tools, such as TLEV and MTLEVs, are independent variables whereas the financial performance tools, including ROE, Tobin's Q and ROA (asset return) are considered dependent variables In addition, the tangibility of assets, growth opportunities, company age and size are control variables After examining the relation between capital structure and corporate performance using the grouped OLS regression model, the model RE and FE are selected to address unexpected heterogeneity Additionally, to regulate the phenomenon of heteroscedasticity in the Model FE, error of FE model cluster is used Furthermore, this research takes the GMM approach to control the presence of the endogeneity problem While different models are used in this study, all results from these approaches are consistent and in line with expectations
This research proves the negative effect of capital structure on corporate performance
In particular, in the study of the linear relationship between the leverage and the performance of the firm, the findings show that both book- and market debt ratios have a negative and considerably related to ROA, ROE and Tobin Q However, the results differ from the Return on Assets and the Tobin Q on financial performance Even though these results do not meet the H1 hypothesis, they still support the trade-off theory sufficiently Moreover, the research results show, when testing a nonlinear relation between capital structure and business performance, that a non-linear relationship is established only If firm performance is measured by the ROE and the capital structure by the total debt ratio
These study results may be incompatible with studies by Berger and Bonaccorsi di Patti (2006), Gill, Biger, and Mathur (2011) and Margaritis and Psillaki (2010) which indicated that the positive correlation between corporate financial performance and capital structure, but most of the studies by Majumdar and Chhibber (1999), Tian and Zeitun (2007) and Joshua (2007) are consistent This is because Harris and Raviv (1991) claimed that a high debt ratio would decrease firm financial performance if the company employs more debt than the standard level and underrate the cost of bankruptcy for assets liquidation or reorganization In addition, a large debt cash flow can encourage managers to conduct discretionary financial conduct
Moreover, Stulz (1990) notes that debt interest payments can exhaust firm cash flows and reduce profitable investment funds, which have an adverse effect on corporate performance The study can therefore provide sufficient support for the pecking order theory, which encourages companies to use their internal financial sources before further debt issues
From a different perspective, control variables, namely asset tangibility, age and size have a negative effect on ROA and Tobin's Q, whereas growth opportunities have a positive impact on ROA and Tobin's Q, as the study findings prove However, asset tangibility and asset turnover impact ROE positively, whilst ROE is negatively influenced by asset growth, age and size As a consequence, these results do not correlate to the research hypotheses H3 and H4
In general, the paper manages to evaluate the negative relationship between capital structure and company performance The importance of this study, however, is not restricted to this conclusion This study will also give UK CEOs and Chief Financial Officers of manufacturing companies information about their capital structure and will eventually enable them to make wise decisions on their capital structure and to attain better financial results.
Limit
This paper addressed the proposed questions and provided an insight into the negative effect of capital structure on the corporate performance of 30 UK manufacturing companies listed on FTSE100 Firstly, no standard is developed for the total effect of leverage on firm performance Moreover, the research on the correlation of the capital structure with the corporate performance of the UK manufacturing companies listed on the FTSE 100 is inadequate, preventing the researcher from comparing this paper comprehensively with earlier research The small sample size of the study is another drawback This paper includes only
30 of 43 UK production companies listed in the FTSE100 The comparatively small number of subjects studied can be clarified by the lack of certain company data during the time period studied In addition, the quality of testing can also be influenced by the short six-year study duration for the analysis In addition, the collection of data is one of the biggest challenges for many scholars, including the author A few businesses do not report sufficient details on properties The loss of critical data items contributes to missing data values that can impact the reliability of the result
Given that the main objective of this study is to investigate the performance relationships between the capital structure and companies based in the UK and listed in FTSE100, the application of the research results may not be as realistic as projected even though the United Kingdom is a highly developed and market oriented country, although FTSE100 is a Share index including 100 companies with high market capitalization listed on the London Stock Exchange FTSE100 is still regarded as a comparatively low measure compared to other indexes (e.g FTSE
250, FTSE 350), as the exchange rate of the Pound is easily influenced In addition, study with a sample covering a variety of countries will generate more compelling results Therefore, for all of the above reasons, focusing on one country does not provide realistic applications for cross - national research, although the study can provide a more detailed and systematic overview
Lastly, while this research employs many approaches including pooled OLS regression, Fixed Effects, Random Effects and GMM model to identify statistical problems such as heteroscedasticity, unattended effects, and endogeneity problems, those problems, especially the endogeneity, may not be fully addressed and regulated This is primarily due to the Repair Effects and Random Effects models used to classify the underestimated heterogeneity They neglect measurement biases, time factors and reverse causalities, which also lead to statistical problems with endogeneity in financial analysis Furthermore, it's complex and simple to produce inaccurate estimates in the analysis by using GMM estimators (Roodman
In conclusion, a greater population might be involved in potential research in several nations Additionally, other variables should also be defined to give C-level managers deeper insights into the correlation between capital structure and corporate financial performance.
CHAPTER 6: PERSONAL REFLECTION
In order to be a financial manager in my homeland, Vietnam, I will always study and analyze methods to efficiently use cash flow and capital to improve the company's effectiveness there are the reasons why I started this work process on this dissertation with a positive outlook on a matter of my selection After spending much time doing this study, I realized that I can accomplish everything if I identify objectives I am the kind of person who loves learning and frequently finding out more experiences and knowledge not only from class but also from reality I'm really excited about learning relevant things about my major that might support me on the financial sector in the future career
When I began setting the framework for the study, I became conscious of such uncertainties and challenges Therefore, to carry out this study, each step must be carefully followed, forcing me to remain centered and committed to every component of it With the time and commitment to finish my thesis, I would like to be more practical than just to get my Master's degree The analytical objective of this thesis is to highlight the importance of the capital structure and its influence on the competitiveness of manufacturing companies in Vietnam My research is aimed at achieving three objectives: define the correlation between the corporate performance and capital structure; examine the effect of capital structure on corporate efficiency; provide suggestions for an effective capital structure; to support managers to make financial decisions in this sector and maximize firm performance by using the capital structure
At first, I struggled to find a theoretical foundation to facilitate my framework since a theoretical framework is essential to conducting research, which assumes over the result of the research (Dawson, 2005) Some academic databases are explored to access, like SSRN.com, and to relevant papers and documents in the online UWE library I spend time reviewing and reading the related journals in order to gain an insight into the capital structure's background and its effects on businesses , particularly manufacturing companies The majority of the literature that I research is focused primarily on the MM theory by Modigliani and Miller (1958) known as the first researcher, who suggest the capital structure theory Their research established the groundwork for future capital structure studies
I studied the methodology proposed by Jensen and Meckling ( 1976) with a view to emphasizing the impact of capital structures on firm performance Their performance measuring methodology relies on the information in the measurement system and the equipment used The typical metrics shown in this principle included inventory turnover, cash flow, liquidity, capital efficiency and return on investment in the financial analysis to assess performance
Secondary Declaration of research, he most critical aspect of my analysis is to choose a suitable methodology Apparently, through the module Professional and Analysis skills for Financial Managers, I am adequate to grasp certain facets of methodologies such as strategies, approach and data analysis Thus, for my study,
I used the quantitative research approach The next task is to identify the data sources and select techniques for data analysis This has been a time-consuming project that needs attention and dedication I will compile a secondary data of the United Kingdom manufacturing companies listed on FTSE 100 between 2014 and
2019, from annual reports' executive and financial statements Additional raw data from reliable sources including FAME.vn and FT.com will be provided
In conclusion, I structured data in a panel format to exploit estimation advantages of a growing number of observations or degrees of freedom This would enhance the efficiency of estimators Analyzing the collected data, I may evaluate the relationship between corporate performance and capital structure through different statistical analysis models including correlation coefficient and OLS Regression or Non-linear Relationship between corporate performance and capital structure As a result, after I finished this dissertation, I acquired an understanding that helps me appreciate my thesis untouched I may also learn how to self-discipline, plan and develop the time of administration, researching and writing skills that are essential skills for potential employment as a financial manager
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