How firm characteristics affect capital structure.

CHAPTER 1: INTRODUCTION 400 words
1.1. Background 300 words
1.2. Research aim and objectives 100 words
CHAPTER 2: LITERATURE REVIEW 1600 words
2.1. Introduction 100 words
2.2. Financial distress and Trade-off theory 300
2.3.Peck Order theory 300
2.4.Agency cost 300
2.5. Past empirical study 600
CHAPTER 3: METHODOLOGY 1025 words
3.1. Introduction 21
3.2. Research strategy 22
3.3. Research approach 23
3.4. Research design 24
3.5. Model specification 24
3.6. The data 25
3.7. Research sample 26

CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
Capital structure can be defined as the proportions of debt and equity which a firm employs to finance its business activities. In corporate finance, this concept has been one of the most argumentative subjects, since the outstanding study of M&M. In order to identify the determinants which are more likely to have major impact on leverage decision, many theories have been developed in the literature. However, there are still debates between scholars about the determinants of capital structure and how they have effect on capital structure choice. In this paper, the researcher aims to review fundamental capital structure theories and past empirical studies in existing literature

2.2 Financial distress and Trade-off theory
In capital structure theories, financial distress holds an important position. According to Berk and DeMarzo (2007, p. 509), a firm is considered facing financial distress is when it cannot meet or having difficulty to pay off its financial obligations to its creditors. When a firm is required to borrow more debts in order to finance its operations and future investments, this will increase the probability of default on the debt. Consequently, lenders will charge high interests for their fund to compensate the risk of lending. Increase in the cost of capital is one of the costs occurring from financial distress. According to Warner (1997), these costs have significant impacts on the firm’s future investment decisions. Additionally, Increase in costs will probably lead to some cuts in research and development activities, advertisement and educational expenditures. Reduce fund for future investment or R&D activities will have negatively effect on the firm’s future development. Consequently, firm’s value will decline and therefore shareholders’ wealth maximization will not be achieved. Financial distress can lead to the arising of two types of costs which are direct and indirect cost. Direct costs include the costs of paying lenders higher rates of interest to compensate them for higher risk and, if forced in to liquidation, the cost of employing lawyers and accountants to manage the liquidation process. Indirect costs include the loss of sales and goodwill as a consequence of operating the company at extreme levels of financial distress. Indirect costs also arise from firm’s decision-makings due to financial distress. “These are changes in investment policy such as, postponing future positive NPV investments or totally discarding investment opportunity, decrease in staff educational expenditures and reducing research and development and marketing activities” (Akdal, 2008). The theoretical underpinnings of capital structure suggest a negative association between financial distress costs and leverage.
The Trade-off theory in capital structure refers to the proportion of debt finance and equity finance which a firm will choose by balance the costs arising from financial distress and benefits gain from interest tax shield. The optimal capital structure can be achieved when the marginal value of the benefits received from debt issues exactly offsets the increase in the present value of the costs associated with issuing more debt (Myers, 2001). The benefit of debt is the tax deductibility of interest payments. Another benefit of debt is that it reduces the manager-shareholder agency conflict. “Corporate managers have the incentive to waste free cash flow on perquisites and bad investment. Debt financing limits the free cash flow avail ble to managers and thereby helps to control this agency problem” (Jensen and Meckling,1976). The costs associated with issuing more debt are the costs of financial distress (Modigliani and Miller, 1963) and the agency costs triggered by conflicts between shareholders and debtors (Jensen and Meckling, 1976). Costs of financial distress are likely to arise when a firm uses excessive debt and is unable to meet the interest and principal payments. Therefore, in order to optimize a firm’s overall value, managers should focus on this trade-off when choosing how much debt and equity to use for financing. This theory also states that firms with high profitability are more likely to have higher gearing in order to shield more taxable income. However, the results from the studies of Kester (1986) and Titman and Wessels (1988) prove this theory fails in some cases and show that there is a strong inverse relationship between profitability and leverage. Similar results are also found in the study of Rajan and Zingales (1995). In addition, Bevan and Danbolt (2002) suggest that the trade-off theory has some shortcomings and limitations. As a result, due to shortcomings and flaws of trade-off theory, the theory is not adequate when determining the ideal capital structure.
2.3 Pecking Order theory
The Pecking order theory conflicts with the idea of firms having a unique proportion of debt and equity finance which minimise their weighted average cost of capital. According to the theory, when a company is looking at financing its long-term investment, it has a well-defined order of preference with respect to the sources of finance available to it. It would prefer retained earnings as a primary source of internal financing. If internal finance is insufficient, bank-borrowing and corporate bonds are the preferred source of external source of finance. After exhausting both of these possibilities, the final and least preferred source of finance is issuing new equity capital.
Initially, these preferences can be explained by the involvement if the issue costs and the ease with which sources of finance are accessed. Retained earnings are the retained profits which will be used to finance firm’s future investment therefore it is readily accessible. There is no issue costs involved and by utilising these earnings, firms do not have to deal or negotiate with third parties such as banks. As for the choice between debt and equity finance, the cost of issuing new debt is much smaller than the cost associated with issuing new equity. If firms need to raise a small amount of fund, it is easier for them to issue debt than equity. Additionally, the issue of debt avoids the potential ownership issues associated with new equity
Myers (1984) put forward a more sophisticated explanation for the existence of the pecking order theory. He explained that the existence of asymmetry of information between the company and the capital market created the order of preference. For example, suppose that a firm is required to raise fund a new project and the benefit of the project is underestimated by the capital market. The company managers, with their inside information, will be aware that the company is undervalued by the market. They will therefore prefer to finance the project through retained earning so that existing shareholders will be benefit when the capital market clearly perceives the true value of the project. If retained earnings are insufficient, manager will choose debt finance in preference to issuing new shares as they will not want to issue new shares if they are undervalued by the market. The opposite is true if the company considers the capital market to be overvaluing its shares in the light of new project they are about to accept. In this situation it will prefer issue new shares at what it considers to be an overvalued price
Myers and Majluf (1984) examined the relationship between profit and companies’ gearing levels and found a significant negative relationship between high profits and high gearing levels. This finding contradicts the idea of the existence of an optimal capital structure and gives support to the insights offered by Pecking order theory. In addition to this, the studies of Ozkan (2001), Kester (1986) and Titman and Wessels (1988) support inverse relationship between leverage and profitability. As a result, pecking order theory is much more accurate in order to explain reverse relationship of profitability and debt ratios rather than trade-off theory.
2.4 Agency cost
The agency theory has its roots in economic theories and it is mostly frequently used to explore the subject of corporate governance. Jesen and Meckling (1976) define the agency theory relationship in term of contract under which one or more persons (the principle) engage another person (agents) to perform some service on their behalf which involves delegating some decision-making authority to the agent. However, managers have conflict of interest with those of shareholders, which is recognised as starting point of agency problem. Agency problem is said to occur when managers make decisions that are not in consistent with the objective of shareholder wealth maximization. There are three important reasons that contribute to the existence of agency problem within public limited companies. The first feature is the divergence of ownership and control. Shareholders are the ones who own the company however they do not run it. Agents (managers) are appointed by shareholders to run the company on their behalf. The second reason is that the goals of the managers (agents) differ from those of the stockholders (principals). Therefore, managers are more likely to look to satisfy their desire for status, power and job security or personal benefit rather than maximizing the shareholders’ value. Asymmetry of information exists between agent and principal is considered as the third reason contributing to the existing of agency problem. Managers, as a consequence of running the company on a day-to-day basis, have access to management accounting data and financial report, whereas shareholders only receive annual report, which may be subject to manipulation by the management. Furthermore, Denis (2001) states that conflict between principals and agents are due to managerial risks aversion. Principals always desire to diversify their investment projects in order to reduce the risk of their investment. However, the majority of agents’ incomes are bonuses, which depend on the returns of company. Therefore, the level risk that principals and agents can bear is quite various. When these reasons are considered together, it should be clear that managers are in position to maximise their own wealth without necessarily being detected by the owners of the company
A study of Jensen (1986) suggested that firms with high level of excess cash are more likely to experience agency cost. When the excess cash decrease, firms have to take in more debts to finance it future investment. Therefore, the availability of money for future spending will be limited. Hence, managers will manage their firms more attentively and efficient in order to avoid financial distress and this reduces the possibility of experiencing agency cost. In reality leverage is used as a tool for providing motivation and discipline for management and minimising agency cost. In conclusion, there is a negative relationship between leverage and agency cost
2.5 Past empirical study
There are many studies in existing literature were conducted to exanimate the determinants of firm’s capital structure. However, there is still no generally accepted model between scholars. Different characteristics and leverage ratios have been examined by researchers in their studies. This part of the dissertation will review past studies to find out what was previously done by scholars
2.5.1 Leverage
Capital structure can be defined as the mixture of firm’s capital with debt and equity. Leverage ratio is commonly used as a proxy of capital structure. Leverage can be defined in several different ways. In existing literature, there are numerous leverage measures used by researchers. Han-suck Song (2005) used three leverage measures as dependent variables in his study. These leverage ratios were total liabilities-to-total assets, debt-to-total assets, debt-to-capitalization. Suhaila and Wan (2008) used total debt-to-total assets as a proxy of leverage in their study about the effect of firm’s characteristics and capital structure of Malaysian listed companies. Pandey (2001) examined six measures of leverage in his study including long-term debt to total assets ratio, short-term debt to total assets ratio and total debt to total assets ratio. Total assets were examined in both market value and book value. Akdal (2011) also investigated six measures of leverage using both book value and market value of total assets in the analysis of UK listed firms. The difference in leverage ratios used by previous studies can be explained by the by the difference in leverage formula as well as the measure of debt used. Debt could be divided into its various components such as short-term debt, long-term debt or total current liability. Some studies used only long-term debt whereas others investigated both short-term debt and long-term debt (Nicolaos, 2007). Although the strict notion of capital structure refers exclusively to long-term leverage, they decided to include short-term debt as well. This is mainly because they believed that firms may roll over short-term debt for long-term finance purpose as flexibility and tax rate. Additionally, it can be difficult for small firms to obtain long-term debt from lenders due to high default risk associated with these firms. As a result, these firms will finance their long-term project by short-term loans.
The leverage ratio of total debt to total assets will be used as a dependent variable in this research. Due to the limitation of collecting market value of total assets, only book value of total assets will be investigating
2.5.2 Firm’s characteristics
The selection of independent variables is primarily guided by the results from previous empirical studies in the context of some developed and developing countries. Each researcher takes into consideration different firm’s characteristics that impact the level of debt ratio (table 1). Therefore, there is still no generally accepted model on determinants of capital structure. In this research, the most common and affecting characteristics; profitability, size, growth, asset tangibility, non-debt tax shield, effective tax rate and liquidity are suggested as independent variables.
Table 1: Past empirical studies
Research Data period Focus Sample size Characteristics

Akdal( 2011) 2002-2009 UK listed companies 1616 P,S,G,T,N,V,L
Song (2005) 1992-2000 Swedish firms 54000 P,S,G, T,N,V,U
Samuel and Song(1042) Up to 2000 Chinese listed companies 1000 P,S,G,T,N,V,Ta
Pandey 1984-1999 Malaysian firms Unknown P,S,G,T,R
Husni and Ali (2010) 2001-2005 Jordanian industrial companies Unknown P,S,T
Nikolaos 1997-2001 Greek listed companies 1419 S,G,L,I
Nahum, Nam and Quyen(2007) 2002-2003 Vietnamese firms Unknown P,S,G,T,N
Suhaila and Wan 2000-2005 Malaysian listed companies 102 S,G,I,L
Ozkan (2001) 1984-1996 Non-financial UK companies 390 P, S, G, N
Cesario, Paulo and Guilherme 1994-2004 Small & medium Europian firms 13070 P,S,G,T,Ta
Liu and Ren (2009) 2004-2007 Listed Chinese IT Companies 92 P, S, G, T L
Linh (2014) 2008-2012 Technology firms listed on OMX Helsinki Stock Exchange 85 P, S, G,L,V,T,N
Deari and Deari (2009) 2005-2007 Listed and Unlisted Macedonian Companies 32 P,S,G,N
Oztekin (2009) 1991-2006 Non-financial firms in the Compustat 15177 S,T
Global Vantage Database
Ramachandran and Packkirisamy (2010) 1996-2007 Indian companies 73 P,S

G: growth opportunity, S: size, A: age, L: liquidity, T: tangibility, N: non-debt tax shield,
Ta: effective tax rate, P: profitability, V: volatility, U: uniqueness, R: risk,
I: interest coverage ratio
2.5.2.1 Firm Size (S):
From table 1, it can be seen that firm size is considered as potential explanatory determinant of differences in leverage among the firms in existing literature. Many past researches suggested that leverage ratio may be affected by firm size. However, the correlation between firm size and debt ratio is still ambiguous. There are two contradicting arguments. The first argument is that size positively impact on debt ratio. Nikolaos (2011) argued that size is closely associated with risk and bankruptcy costs. Larger firms usually have lower default risk in compare to smaller firms because larger firms are more diversified. Additionally, he found large firms usually request larger amounts of debt than smaller firms. As a result, these firms usually take the advantage of reduction in transaction costs associated with long-term debt issuance and can be able to negotiate a lower interest rate. Therefore, large firms tend to use higher amount of debt. Likewise, Nahum, Nam and Quyen (2007) argued that large firms enjoy economies of scale and creditworthiness in issuing long-term debt and have strong bargaining power over lenders. By analysing Vietnamese listed firms, they also found that firm size positively affect debt ratio. Similar results are also found by Akdal (2011), Huang & Song (2006) and Pandey (2001). The trade-off theory also supports that firm’s size should have positive relationship with leverage. On the contrary, few empirical studies discovered that firm size is negatively correlated with leverage such as Kester (1986), Titman and Wessels (1988). The pecking order theory suggests that bigger firms are tend to use less debt than small firms due to asymmetric information problems
In empirical studies, firm size is measured in many different ways. Husni and Ali (2010) and Pandey (2001) used the natural logarithm of total assets as a measure of firm’s size. Karadeniz (2009) measured firm’s size by net sales adjusted by inflation rate. Batholdy and Mateus (2005) used the natural logarithm of total assets as a proxy for firm size. In this research, the natural logarithm of total assets (SIZE) will used as a proxy for firm size and it is expected that firm size will positively correlated with leverage.
Hypothesis 1:
H0: There is a positive relationship between size and leverage
H1: There is a negative relationship between size and leverage
2.5.2.2 Profitability (P):
The predicted relationship between a firm’s profitability and its leverage in existing literature has been mixed. According to the interest tax shield hypothesis, the presence of taxes would induce firms with high profitability to use more debt instead of earnings in order to take advantage of tax deductibility of interest paid on debt (Nahum, 2007). According to agency cost theory, firms with high profitability indicate that there are high levels of free cash flows. Therefore, more debts should be employed to discipline management attitudes, ensure that managers pay out profits (Jensen 1986.) Overall, it is suggested that profitability negatively influence leverage. On the other hand, the pecking order or asymmetric information hypothesis of Myers and Majluf (1984) states that profitable firms tend to use less debt because these firms will utilize their retained earnings to pay up debt in order to overcome possible restraints on management discretion. A majority of empirical studies support the pecking order theory (Kester, 1986; Titman and Wessels, 1988; Ozkan, 2001; Linh, 2014). Long and Malitz (1985) study reported a positive relationship between leverage and profitability, and however, the statistical evidence was weak.
The proxy to define firm’s profitability is also various in previous works. Some possible proxies can be the operating income over sales (Titman &Wessels 1988), return on assets (ROA) (Nahum, Nam and Quyen, 2007), EBIT over total assets (Huang and Song, 2006; Kirch, Mateus and Terra, 2012), earnings before interest, tax and depreciation over total assets (Akdal, 2011). In this paper, the ratio of earnings before interest and tax to total assets is assumed as measure to profitability. It is expected that there is a negative correlation between profitability and leverage. The ratio of earnings before interest and tax to total assets is assumed as measure to profitability.
Hypothesis 2:
H0: There is a negative relationship between profitability and leverage
H1: There is a positive relationship between profitability and leverage
2.5.2.3 Growth opportunity (G):
There is a large uncertainty associated with the growth factor, both regarding its effect on leverage and how it shall be measured. First, we may expect a positive relationship between growth and leverage. Firms with high growth opportunity usually need to increase their fixed assets. Therefore, these firms will have greater future demand for funds as well as retaining more earnings (Pandey, 2001). According to trade-off theory, firms with high growth opportunity will increase their return earnings and issue more debt to maintain the target leverage. Thus, it is expected that growth opportunity to be positively related to debt ratio based on this argument. Pecking order theory also supports the same relationship. According to the theory, growth causes firms to shift financing from new equity to debt, as they demand more funds to reduce the agency problem. The same relationship is also found in the study of Huang and song (2002), Pandey (2005). On the contrary, Myers (1977) argues that “firms investing in assets that may generate high growth opportunities in the future face difficulties in borrowing against such assets” (Song, 2005). For this reason, he is expected a negative relationship between growth and leverage. Akdal (2011) also suggested that there is inverse relationship between growth and leverage. However, some studies discovered that that there is no significant relationship between growth opportunity such as Song (2005) and Linh (2014).
There are different proxies for growth opportunities with different implication. Titman and Wessels (1988) use capital investment to total assets ratio and research and development over total sales to proxy growth opportunities. Rajan and Zingales (1995) use Tobin’s Q and Booth et al. (2001) use market-to-book ratio of equity to measure growth opportunities. According Bartholdy and Mateus (2008), intangible fixed assets-to-total assets ratio is an indicator of expected growth because he found that growth opportunity is associated with intangible fixed assets of the firm. Other measures of growth include the percentage change in total assets (Nahum, 2007), annual change in earnings (Nikolaos, 2011), market-to-book ratio (Akdal, 2011). In this paper, in the line with Bartholdy and Mateus (2008) an intangible fixed asset over total assets is employed to measure growth and it is expected that growth opportunity is positively impact on leverage.
Hypothesis 3:
H0: There is a positive relationship between growth opportunity and leverage
H1: There is a negative relationship between growth opportunity and leverage
2.5.2.4 Non-debt tax shield (NDTS):
Non-debt tax shield are the tax deduction for depreciation and investment tax credits. DeAngelo and Masulis (1980) argued that non-debt tax shields are considered as substitutes for the tax benefits of using debt financing. Firms are expected to use less debt if they have large non-debt tax shields. Their predicted relationship between non-debt tax shields and leverage is also supported by Ozkan (2001), Akdal (2011). Hence, it is expected that an increase in non-debt tax shields will cause a decrease in leverage.
Empirical studies use different indicators as a proxy for non-debt tax shield, including ratio of depreciation and amortization expenses scaled by total assets (Huang & Song, 2006), the ratio of tax credits over total assets and the ratio of depreciation over total assets as measures of non-debt tax shield (Titman and Wessels ,1988). In this research, the ratio of depreciation over total assets will be used as a proxy for non-debt tax shield.
Hypothesis 4:
H0: There is a negative relationship between NDTS and leverage
H1: There is a positive relationship between NDTS and leverage
2.5.2.5 Asset tangibility (T):
Tangibility refers to the level of fixed assets over firm’s total assets. These tangible assets are also defined as property, plants and equipment. Theories generally support that tangibility is positively correlated with leverage. Firms with high amount of tangible assets are considered as less risky by finance providers. According to trade-off theory, these assets can be used as collateral and provide security to lenders in the event of financial distress. “Collateral also protects lenders from moral hazard problem caused by the shareholders-lenders conflict” (Pandey, 2001, p4). Hence, a high fraction of tangible assets is expected to be associated with high leverage. Empirical studies that confirm the above theoretical prediction include Titman and Wessels (1988), Akdal (2011), Song (2005), Friend & Lang (1988), Wald (1999), Rajan & Zingales (1995).
In this study, tangibility is measured as fixed assets scaled by total assets.
Hypothesis 5:
H0: There is a positive relationship between asset tangibility and leverage
H1: There is a negative relationship between asset tangibility and leverage
2.5.2.6 Liquidity (L):
Liquidity ratio can signify different signals to different stakeholders of the business. From institutional investors’ viewpoint, this might signal a negative situation which the firm might have some problems with long-term investment because too much cash and cash equivalent are being kept as current assets. Another problem associated with high level of current assets is that it might signal to investors that there are too much remaining unsettled account receivables in the balance sheet which can turn to bad debt. In contrast, from lenders’ point of view, firms with high liquidity will have low default risk in paying off short-term obligations. This is because these firms can quickly convert its current assets in to cash to fulfil its debt obligations (Brigham & Houston 2007, 87-88.). Therefore, firms with high liquidity appear to have low default risk, they can employ more debt if the level of liquidity is high. Accordingly, it suggests that liquidity ratio has positive relationship with firm’s leverage. However, there is a debate for a negative relationship between them. Akdal (2011) found that liquidity is significantly related to its capital structure. More specifically, there is a negative relationship between a firm liquidity and its leverage. He explained that the more debt the firm uses the more current liabilities this will imply and the fewer current assets will remain after dealing with the liabilities leading to the decrease in liquidity. The negative relationship between liquidity and leverage is also confirmed by Linh (2014) and Ozkan (2001).
Akdal (2011), Ozkan (2001) and Linh (2014) used the proportion of current assets to current liabilities as a proxy for liquidity. The same proxy for liquidity will be used in this research and it is expected that liquidity is positively related to leverage.
Hypothesis 6:
H0: There is a positive relationship between liquidity and leverage
H1: There is a negative relationship between liquidity and leverage
2.5.2.7 Effective tax rate (Tax):
Corporate income tax has important impact on debt–equity choices. The Modigliani–Miller proposition (1958) suggests that firms that face higher marginal tax rates would use more debts to obtain a tax-shield gain. In a study conducted by Mackie & Jeffrey (1990), they found that debt have a positive effect on marginal effect tax rates. Similarly, Huang and Song (2006) reported the same results when he used average effective tax rate to examine the Chinese listed firms. Although all researchers believe that taxes must be important to companies’ capital structure, many of their studies fail to find plausible or significant tax effects on financing behaviours. This is because the debt/equity ratios are the cumulative result of years’ of separate decisions and most tax shields have a negligible effect on the marginal tax rate for most firms (MacKie-Mason, 1990)
Empirical studies provide several ways of measuring effective tax rate. Bartholdyn and Mateus (2008) used earnings before taxes minus net earnings divided by earnings before taxes as a proxy for tax rate. Nahum, Nam and Quyen (2007) used average effective income tax rate as a proxy for tax rates to examine the effect of tax on leverage. Linh (2014) used the ratio of income tax over earnings before tax (EBT) as a proxy for effective tax rate in his study. The proxy used by Linh will be examined in this study and it is expected that the relationship between effective tax rate and leverage is positive.
Hypothesis 6:
H0: There is a positive relationship between tax and leverage
H1: There is a negative relationship between tax and leverage
2.6 Hypotheses development
The hypotheses which were developed in the literature review will be summarised in the following table.
Table 2: Hypotheses
Characteristic Hypothesis
Firm size (SIZE) H0: There is a positive relationship between size and leverage
H1: There is a negative relationship between size and leverage
Profitability (P) H0: There is a negative relationship between profitability and leverage
H1: There is a positive relationship between profitability and leverage
Growth opportunity (G) H0: There is a positive relationship between growth opportunity and leverage
H1: There is a negative relationship between growth opportunity and leverage
Non-debt tax shield (NDTS H0: There is a negative relationship between NDTS and leverage
H1: There is a positive relationship between NDTS and leverage
Asset tangibility (TANG) H0: There is a positive relationship between asset tangibility and leverage
H1: There is a negative relationship between asset tangibility and leverage
Liquidity (L) H0: There is a positive relationship between liquidity and leverage
H1: There is a negative relationship between liquidity and leverage
Effective tax rate (TAX) H0: There is a positive relationship between tax and leverage
H1: There is a negative relationship between tax and leverage

CHAPTER 3: METHODOLOGY
3.1 Introduction
According to Saunders et al. (2009), methodology refers to the theory for how research should be undertaken. Ghauri and Gronhaug (2005) defined methodology as an important tool in the researcher’s toolbox. As such, this chapter discusses the most suitable research method to achieve the aim and objectives of this research. This chapter provides an explanation of data collection, strategies, research design, methods of data analysis and limitations.
3.2 Research strategy
Research strategy is a general direction to the conduct of business research (Bryman and Bell, 2007). On the other hand, research strategy refers to a structured plan of how the researcher will answer the hypothetical propositions (Saunders et al., 2009). There are two main research strategies namely, quantitative and qualitative. It depends on the need of the researcher to follow quantitative, qualitative or mix of both strategies that best serves the purpose of this study. Quantitative research is concerned with analysis of quantifiable data and can be used to identify causal relationship between variables (Bryman and Bell, 2007). In contrast, qualitative research emphasizes on the words in the collection and analysis of data, by using interpretative methods (Collis and Hussey, 2009). Several researchers have explored the differences between quantitative and qualitative research strategies. In the table below the differences between two types of research strategies are demonstrated.
Figure 1: Some common contrast between quantitative and qualitative research.
Quantitative Qualitative
Numbers Words
Point of the view of researcher Point of the view of participants
Researcher distant Researcher close
Theory testing Theory emergent
Static Process
Structured Unstructured
Generalisation Contextual Understanding
Hard, reliable data Rich, deep data
Macro Micro
Behaviour Meaning
Artificial settings Natural settings

Furthermore, the most advantage of quantitative is that results can generally be examined as more representative thanks to the use of larger samples and greater emphasis on achieving validity and reliability (Matveev, 2002). In addition, results of quantitative research are stated to be relatively more objective than qualitative (Hair et al., 2011). Moreover, the aim of this study is to examine how firm’s characteristics have effect on capital structure decision, which means identifying the relationships among variables. Therefore, in the context of this study, the quantitative research was deemed appropriate. In the next section, it will be discussed which type of research approach will be chosen.
3.3 Research approach
There are basically two research approaches namely inductive and deductive that involves a relationship between theory and research (Bryman and Bell, 2007). An inductive approach refers to the development of theory as ‘’a result of the observation of empirical data’’ (Saunders, Lewis and Thornhill, 2009). This means that the data are collected without the existence of a theory or pre-defined hypothesis and research is employed to generate theory. On contrary, in deductive approach, the theory and hypotheses are subject to empirical research which follows to confirm or reject the already existence of theoretical framework. To accomplish the objective of this study, deductive approach has been selected by several reasons. Firstly, quantitative research uses a deductive approach, which means, ‘’to develop a theory and hypotheses in term of theory testing (Bryman and Bell, 2007, Saunders et al., 2007). Secondly, there is no need in developing a new theory on capital structure and its determinants since there is plenty of existing theory frameworks on capital structure that commenced by literature review section. Finally, deductive approach is mainly employed to investigate causal relationship between variables (Saunders et al., 2009). However, some researchers argue that the downside of deductive is that it restricts itself too much and does not allow flexibly for alternative explanations (Saunder et al., 2009). This is best illustrated in the figure below:
Figure 2: The process of deduction

Source: Bryman and Bell, 2007

3.4 Research design
According to Bryman and Bell (2007), research design is ‘’ a framework for the collecting and analysing the necessary data for particular study’’. Research design consists of five major designs such as experimental, longitudinal, cross-sectional, case study and comparative design. In this study, the cross-sectional research design was choose by several reasons. In this research work, I will gather quantitative data from financial statements of 115 companies listed on S&P 500 in the period from 2008 to 2013 to find the relationship between firm’s characteristics and leverage
3.4.1 Dependent Variable
In this paper, leverage is the dependent variable. The measure of leverage will be used is the ratio of total debt over total assets
LV = Total Debt (Short-term + Long-term) /Total Assets
3.4.2 Independent Variables
In this paper, profitability, size, growth, tangibility, non-debt tax shield, volatility and liquidity are suggested as independent variables. Proxies and expected relationship of each independent variable are summarised in table 2
Table 2: Independent variable summary

Variable Proxy Expected relationship
Pos (+) & Neg (-)
Size Natural log of total assets +
Growth opportunity Intangible fixed assets / total assets +
Profitability EBIT / total assets –
Non-debt tax shield Depreciation / total assets –
Asset tangibility Fixed assets / total assets. +
Liquidity Current assets / current liabilities +
Effective tax rate (Income taxes)/ EBIT +

3.5. Model Specification
This study has tried to determine the relationship between firm’s characteristics and capital structure. Given the number of independent variables, a multi regression model was used to analyse the data and relationship the variables. In order to investigate relationship between leverage and independent variables, the model that was used by Titman and Wessels (1988), Akdal (2011) Rajan, Zingales (1995) is used. Therefore, the data from DataStream would be analysed based on following empirical model.
Leverage = β0 + β1*Size + β2*Growth + β3*P + β4*Tang + β5*Ta x+ β6*NDTS + β7*Liquidity + ε
Where,
β0: Constant ß: Regression coefficient
P: Profitability S: Size
G: Growth Opportunity T: Tangibility
N: Non-debt Tax Shield Ta: Effective tax rate L: Liquidity
ε: The error term
In some past empirical studies, the pooled ordinary least square method was used in order to estimate the coefficients of independent variables. Following these studies, the same method will be used in this research
In this model, the impact of firm’s characteristics on capital structure will be examined in line with the previous model used by previous researchers mentioned above. The leverage ratio used in this model is the ratio of total debt over total assets. Annual financial information of 115 listed firms from the period from 2010 to 2013 will be analysed by using SPSS.
3.6 Data
According to empirical studies, the required data for this research is quantitative data from financial materials which can be collected various financial databases such as DataStream, Google finance, Thomson… etc. Sample of this study is comprised of 115 listed companies from S&P 500.
3.6.1 Secondary data
Primary data consists of the process of collecting new data for a specific purpose, whereas secondary data consists of collecting data that already been collected previously for some other purpose (Saunders et al., 2009). There are many advantages from this method in research. The advantage of secondary data provide high quality information at a cost efficiency and time manner if electronic database such as DataStream, Emerald, Sciencedirect, ABI Proquest and academic book are being used, particular as a student research with limited resources (Bryman and Bell, 2007). However, sometime secondary data collected for research work may not fit with the structure and objective of this particular study (Bryman and Bell, 2007). Furthermore, data may be not collected from the accurate and available sources. For this study, secondary data is sourced mostly via DataStream, University of Greenwich Portal and internet. Academic journal and textbook, newspaper articles are reliable sources for recent material on business issues and have been employed extensively in the review literature in past researches. Quantitative data which is the main source of this study is required investigating the relationships in significant level will be collected from financial databases via DataStream. It is time consuming and sometimes costly to observe the financial information of each company in the sample. Therefore, using financial databases such as DataStream would save time and provide more accurate and reliable results.
3.6.2 Data Collection
For the purpose of this paper, the data is collected from secondary sources solely based on DataStream. The financial information of listed US companies will be analysed on SPSS to examine whether there is significant correlation between leverage and its determinants.
3.7 Research sample
According to Bryman and Bell (2007), ‘sample is the proportion of population chosen for a research to provide a representative of a population’. Therefore, choosing an appropriate sample is the primary element for all successful research (Collin and Hussey, 2003). For this research, the sample size consists of financial figures of 115 companies listed on S&P 500 over the period from 2010 to 2013 giving the total sample size of 460. These companies were randomly chosen. Therefore, the final sample provides wide range of companies from numerous sectors. In order to obtain the best results, companies that have missing financial information for any period of time within 2010-2013 will be eliminated.
3.8 Data analysis
According to Bryman and Bell (2011), the quantitative data, from the financial materials, is analysed by the Statistical Package for the Social Sciences (SPSS) software version 21. SPSS allows researcher to examine the patterns between variables, thus further investigating if one variable impact another. Furthermore, SPSS is the most popular software tool to analyse data because it helps in generating descriptive statistics such as mean, regression, and correlation, etc (Robson, 2002).

Are you looking for a similar paper or any other quality academic essay? Then look no further. Our research paper writing service is what you require. Our team of experienced writers is on standby to deliver to you an original paper as per your specified instructions with zero plagiarism guaranteed. This is the perfect way you can prepare your own unique academic paper and score the grades you deserve.

Use the order calculator below and get started! Contact our live support team for any assistance or inquiry.