Impact of Financial Leverage on Investment in Financial and Non-Financial Firms in the UK

ABSTRACT

This study investigates the impact of financial leverage on investment in financial and non-financial firms listed in the London Stock Exchange (LSE). Using descriptive statistics, correlation analysis and pooled ordinary least square regression we develop a model with a set of control variables to identify both the unique and complementary effects of leverage on investment in a sample 20 financial & non-financial firms listed in the LSE between 2011 and 2015. Our empirical findings reported that financial leverage has an overall insignificant relationship with firms listed in the LSE. Interestingly, while financial leverage was found to have a positive and significant relationship with investment among financial firms listed in the LSE, the variable had a positive but insignificant impact on investment among non-financial firms listed in the LSE.

 

CHAPTER ONE

INTRODUCTION

  •       Background

In the wake of the global financial crisis in 2008, debt-financing has been generating much attention among financial analysts due to the fact that excessive debt leveraging of firms capital structure was part of the reasons adduced for the economic downturn. High and sometimes excessive leveraging in the period leading up to the financial crisis led to increases in risk premiums as well as required returns by shareholders (Karma and Sander, 2006). A number of financing options are open to organisations with regards their ‘capital structure’ which refers to how firms choose to finance their activities and growth through different sources of funds (Ahn, Denis, and Denis, 2004). These options may include debt which comes in the form of bond issues or long-term notes payable or equity classified as common or preferred stock or through the use of retained earnings.

Financial leverage refers to the use of debt capital or the degree to which a company uses fixed income securities such as debt and fixed equity to fund its objectives (Matemilola, Bany-Ariffin., and Azman-Saini, 2012). Financial leveraging has been a central issue in finance and investments stemming from a study by Modigliani and Miller (1958). The decision to finance a firm’s activities using financial leverage or another source of funds represents one of the key corporate investment decisions and ultimately, serves to determine the future financial health of the firm. As observed by Amir, Hazoor, and Sabir (2016: 83), firms could be divided into two broad categories with regards to their growth capacities; those with high growth and those with low growth. While those firms with high growth in terms of financial capacity and activities can take advantage of investment opportunities by adopting the financial leverage option as a result of having adequate cash flow to withstand any risks arising from such choice, low growth firms’ considerable difficulties in adopting financial leveraging as a financing means owing to their low debt carrying capacities. The low financial capacities of low growth firms render them mostly incapable of generating enough resources to pay off debt as well as generate income in a reasonable time (Amir, Hazoor, and Sabir, 2016).

Investment generally refers to any mechanism used for the purpose of generating future income or more specifically defined, an increase in the physical capital stock of a company that will be used to increase the company’s productive capacity. Investments may include the purchase of bonds, stocks or real estate property, construction of a building or other facility used to produce goods or the production of goods required to produce other goods. (Ahn, Denis, and Denis, 2004), are of vital importance not just in determining firm capital structure but also, in determining its profitability and long-term future. Over the years as observed by Buell (1981), financial leveraging has been on the rise among firms in the US and most of the developed world. According to Buell (1981: 66),

“In 1952 the average U.S. manufacturing firm had a financial structure of 20.2 percent current liabilities, 15.5 percent long-term liabilities and 64.3 percent equity. The average before-tax return on investment was 12.5 percent and the average return on the book value of equity was 8.1 percent. By 1980 the average financial structure had dramatically changed: current liabilities, 30.8 percent; long-term debt, 24.2 percent; and stockholders’ equity, 45.0 percent. Accompanying this increase in financial leverage was an improvement in the return on the equity to 12.2 percent despite a drop in the return on assets to 11.2 percent.”

From Buell’s study, while there was a marked improvement in return on equity, return on assets also dropped considerably as a result of the economic downturn that characterized the period of study. Other studies have also investigated the effects of leverage on firms with often contradictory results indicating a yet unclear impact of leverage on firms. Thus, this study sets out to examine the impact of financial leveraging on investment in both financial and non-financial firms listed in the London Stock Exchange by examining the impact of various financial leveraging variables on firm investment.

1.2            Statement of the Problem

A recurring and central issue in finance has been whether financial leverage affects investment and investment policies of firms. On one side of the divide are financial analysts that argue that a firm’s capital structure is essentially irrelevant. According to these set of analysts, a firm that has invested in good and profitable projects would grow and stay profitable no matter how its balance sheet looks, mostly because it will have no difficulties obtaining any required funding (Lang, Ofekb, and Stulz, 1996: 3). Some analysts like Miller (1991) saw arguments over financial leveraging as a waste of time as they were thought to be self-correcting and are of very little significance. On the flip side, however, researchers on the other side of the divide argue that “high leverage reduces a firm’s ability to finance growth through a liquidity effect” (Lang, Ofekb, and Stulz, 1996: 4). The “liquidity effect” occurs as a result of excessive deleveraging – the forced and rapid paying down of debt which reduces a firm’s ability to finance investment endeavours (Mian and Sufi, 2014; Mian; Sufi and Verner, 2015). As leverage increases, the costs of debt become larger and tends to erode the net benefits to leverage. As observed by Coricelli (2011), “highly-levered firms not only suffer from a debt overhang problem, which reduces their incentives to invest in productive investment, their attention is also diverted from productivity improvements by the need to generate cash flow in order to service their debts”. Myers (1977) in his study, demonstrated that a firm’s debt overhang could be large enough to prevent that firm from raising badly needed funds to embark on other profitable ventures and improve its overall profitability standing. All these points to the desirability of adequate or optimal leveraging in order to obtain the benefits of leveraging by firms.

A systematic process is outlined by Coricelli (2011) for management to follow in order to determine optimal leveraging policy. According to Coricelli (2011), firms should determine their optimal degree of leverage by first deciding how much external capital it will need to raise to operate its business. Once this is done, management needs to examine the financial markets to determine the terms in which the company can raise capital. Determining the terms of raising capital is crucial to the process, as the market environment may curtail the ability of the company to issue debt at an attractive or manageable cost. Once these questions have been answered, management can then proceed to design an appropriate capital structure policy, and construct a package of financial instruments that need to be sold to investors. This study sets out to examine these issues by measuring the level of impact financial leveraging has had on investment among a set of financial and non-financial firms in the UK. By measuring the impact of a set of financial leveraging measures on investment for a group of systematically selected financial and non-financial firms in the UK, this study aims to determine if financial leveraging has hurt UK firms or improved their performances by examining how it has impacted investment.

1.3            Study Objectives

The broad objective of this study is to investigate the impact of firm’s financial leveraging decisions on corporate investment in the UK over a five year period (2011-2015). The more specific objectives of the study include:

  1. To examine the impact of financial leverage on investment of selected firms listed on London Stock Exchange.
  2. To determine the effect of financial leveraging and investment on both financial and non-financial firms in the UK
  3. To determine if cash flow, liquidity, profitability and sales growth are significant variables that contribute to firm investment in the UK.

1.4            Research Questions

The study is guided by the following research questions

  1. To what extent does financial leverage affect investment among firms listed on the London Stock Exchange?
  2. Does financial leveraging and investment have a significant effect on financial and non-financial firms in the UK?
  • Do firm’s cash flow, liquidity, profitability and sales growth affect investment among firms listed on the London Stock Exchange?
  1. Does financial leveraging have a negative or positive effect on investment among firms listed on the London Stock Exchange?

1.5            Research Hypotheses

The following research hypotheses are formulated to test our study:

: Financial leveraging has no significant impact on investment among firms listed in the London Stock Exchange

: There is no significant relationship between financial leverage and investment among financial firms in the London Stock Exchange.

: There is no significant relationship between financial leverage and investment among non-financial firms in the London Stock Exchange.

1.6            Data and Research Methodology

In order to achieve the objectives of the study, secondary data have been employed to run appropriate tests. As the study is conducted on UK firms listed on the London stock exchange, the data for 20 selected firms have been gathered from the annual reports database at www.annualreports.com. Data are collected for a period of five years from 2011 to 2015.

The methodology adopted for analysis in this study is the pooled Ordinary Least Square (POLS) technique which is very suitable for a study of this nature. However, for robustness of this estimation approach, pre-estimation tests are conducted to examine the characteristics of the variables used. Simple descriptive statistics of the variables were also conducted and correlation coefficients estimated to examine the degree of association between variables.

1.8 Scope of the Study

The scope of our study covers a five year period between 2011 and 2015. This period was characterized by relative stability in the UK stock exchanges in the aftermath of the Global financial crisis and before the 2016 UK market turmoil in the aftermath of the BREXIT vote. The period has been carefully selected to avoid periods of severe exogenous macroeconomic disruptions that may have led to economic disruptions for firms listed in the LSE.

REFERENCE LIST

Ahn, Seoungpil and Denis, David J. and Denis, Diane K., Leverage and Investment in Diversified Firms (2004). SSRN Research Papers.  Accessed March 3rd, 2018.

Amir M., S., Hazoor .M. and Sabir M. (2016) ‘Does Financial Leverage Influence Investment Decisions? Empirical Evidence from kse-30 index of Pakistan’. Asian Journal of Economic Modelling, 4(2): 82-89.

Buell S. (1981) ‘Financial Leverage-For Better or Worse?’ The Financial Review. 16 (4), 16.

Karma O., and Sander P. (2006) ‘The Impact of Financial Leverage on Risk of Equity Measured by Loss-oriented Risk Measures: An Option Pricing Approach.’ European Journal of Operational Research. (175) 1340–1356.

Lang .L, Ofekb, E., and Stulz M (1996) Leverage, Investment, and Firm Growth. Journal of Financial Economics. 40, 3-29.

Matemilola B., Bany-Ariffin .A. and Azman-Saini W., (2012) ‘Financial Leverage and Shareholder’s Required Returns: Evidence from South Africa Corporate Sector’. Transit Studies Review (18), 601–612.

Mian, Atif and Amir Sufi. 2014. “What Explains the 2007-2009 Drop in Employment?” Econometrica 82(6): 2197-2223.

Mian, Atif, Amir Sufi and Emil Verner. 2015. “Household Debt and Business Cycles Worldwide.” NBER Working Paper 21581.

Miller, M., (1991). Leverage. Journal of Finance, (44), 479 489.

Modigliani, F. and M.H. Miller (1958). ‘The Cost of Capital, Corporation Finance and the Theory of Investment”. American Economic Review, 48(3): 261-297.

LITERATURE AND EMPIRICAL REVIEW

2.1         Introduction

Decisions and judgment to select the optimal investment financing decision in order to maximize shareholder wealth are two of the most critical decisions financial managers are charged with (Bae & Goyal, 2010). In order to achieve the aforesaid objective, financial managers are charged with increasing income earned from investments while minimizing associated costs in any number of ways. Financing is considered to be a critical factor affecting the success of financial managers (particularly finance directors) as they pursue their objective of increasing shareholder income. Any number of finance options are available to financial managers as identified by Ahmadpoor, Kashani-Poor, and Shojai (2011: 20), these include:

  1. Adoption of capital budgeting,
  2. Restructuring to establish an optimal structure
  • Adopting long-term lease
  1. Issuance of bonds and other debt instruments
  2. Working Capital Management

This study focuses on leverage financing which refers to the use of debt capital and fixed equity to fund investments (Matemilola et al, 2012). Precisely because information is asymmetrical, external financing is more costly than internal cash flow investments as it bears a higher risk (Chiang & Ko, 2009), therefore, it is important that the costs and benefits associated with such an endeavour are properly understood. This chapter discusses issues surrounding the effects of financial leveraging on corporate investment in financial and non-financial firms in the UK. We first examine some of the key concepts, theories, examine the state of leverage financing in the UK before exploring some of the empirical literature drawn from relevant journals and articles.

2.2 Capital Structure and Financial Leverage

According to Acaravci (2015), financial management discipline comprises three key decision functions i.e.

  1. Capital Budgeting/Investment Decisions: Here, financial managers are concerned with the efficient utilization of capital or funds to acquire assets and how these funds/capital are employed to achieve organizational objectives such as shareholder wealth maximization. It is primarily concerned with the nature and composition of assets of a firm.
  2. Asset Management Decisions: This decision area for financial managers deals with the efficient utilization of assets acquired through investment decisions.
  • Capital Structure/ Financing Decisions: Here, financial managers are concerned with the proper selection or mix of capital. Financial managers are concerned with making a choice between debt and equity and involves issues like the degree firms embrace financial leverage.

This study focuses on this third key function of financial managers by first distinguishing the concepts of capital structure and financial leveraging.

2.3 Capital Structure

Various researchers have defined capital structure in the context of financial management. For example, Brealey and Myers (1991) define the concept as comprising debt, equity or hybrid securities issued by a particular firm in order to raise revenue for its business activities while VanHorn (1989) on the other hand, saw capital structure as representing the proportion of debt to total capital of a firm. To Pandey (2005), capital structure represents the choice a firm makes between internal and external financial instruments.

From the various definitions put forward by researchers, we can reasonably conclude that the capital structure of a firm represents the way or manner the firm chooses to raise the capital required to achieve corporate goals. As observed by Pandy (2005), capital structure is a mixture of various types of equity and debt capital maintained by a firm which are the result of the firms financing decisions. Furthermore as observed by VanHorn (1989) capital structure refers to the degree a firm employs a combination of debt and equity to finance its assets. From the foregoing, it can be deduced that the capital structure of a firm comprises the ‘structure’ of its liabilities.

2.3.1         Features of Appropriate Capital Structure

The Board of Directors or financial managers of firms are usually charged with developing the capital structures most beneficial to a firm. Usually, a firm’s capital structure as observed by Acaravci (2015) should be planned with the interest of the equity shareholders and the financial requirements of the company given utmost consideration. Pandey (2005) identifies the following as the features of an appropriate capital structure:

  1. Returns: An appropriate capital structure of a firm should be financially advantageous and subject to other considerations, should generate maximum returns to shareholders with minimal
  2. Risk: Excessive debt threatens company solvency or liquidity of the firm. An adequate capital structure would only employ debt as a source of capital so long as it does not add a significant risk to firm operations.
  3. Flexibility: Capital structure should be ideally flexible in order to adapt to day to day operational realities. As pointed out by Acaravci (2015: 164), “It should be possible for a company to adapt its capital structure with a minimum cost and delay if warranted by a changed situation”.
  4. Capacity: An adequate capital structure should identify and respect the debt capacity of a firm. Usually, an adequate capital structure is determined within the debt capacity of a firm thereby ensuring this capacity is not exceeded. Generally, a firm’s debt capacity is dependent on the firm’s ability to generate cash flows in future (Gropp and Heider, 2007).
  5. Control: An adequate capital structure guards against loss of control of the firm either to debtors or regulatory agencies. Owners of closely held firms pay particular attention to moves that may dilute their ownership or control of the firm.

 2.3.2        Determinants of Capital Structure

Following the seminal work on capital structure by Modigliani and Miller (1958), a number of studies have been directed towards highlighting what actually influences firm capital structure choices. Initial research conducted on the subject as observed by Pandy (2005), were mainly focused on firms in the United States. One of the prominent studies conducted in the United States was carried out by Titman and Wessels (1988) who both examined the theoretical determinants of capital structure by carrying out an empirical study on the subject.

The researchers highlighted firm theoretical attributes like asset structure, growth, firm size, earnings volatility, profitability etc. which were individually tested to see their impact on firm’s debt-equity choice. Their results as pointed out by Pandy (2015), were consistent with extant capital structure theories for the factors affecting firm capital structure choices. An interesting discovery from their study on the US was that debt had a negative relationship with the “uniqueness” of the firm’s line of business. According to the researcher, firms who can impose higher costs on their customers, suppliers and employees due to their specific operations tend to use debt more conservatively and vice versa. This finding is in line with that of Botta (1989). Also, firms short-term debt ratio were found to be negatively related to the size of the firm just as a strong negative relationship was discovered to exist between firm debt ratios and past profitability implying that most firms employ leveraging to make up for shortfalls in profits.

While there was a flurry of studies on the United States, Rajan and Zingales (1995) attempted to broaden the research on the subject by focusing on other developed countries. Rajan and Zingales (1995) drew their sample from the G7 countries comprising US, Germany, Japan, the UK, Canada, France and Italy. The researchers investigated whether or not firm capital structure choices in the United States are peculiar to the United States or where similar across the developed countries as sampled by the G7 countries. Four factors comprising tangibility of assets, firm growth, size as well as firm profitability were tested against leverage to measure their influence on the variable. Rajan and Zingales’ result showed that across sampled countries, asset tangibility (i.e, the resale value or ease of redeployment of the firm’s assets) showed a positive correlation with leverage thereby supporting the notion that firms with a higher level of fixed assets within their asset mix would employ them as collateral to obtain even more loans. Other findings by the study showed that the market to book ratio appeared to have a negative correlation with leverage and also, firms with a high market valuation of their stocks were prone to issue more stocks rather than seek another form of debt arrangements.

Several other studies have been carried out on the subject of capital structure outside the United States. Gropp and Heider’s (2007) study examined bank capital structure by taking a look at banks in the US and 15 EU member states. The study tested the significance of variables such as bank size, profitability, dividend-paying status as well as other variables like market-to-book ratio and asset tangibility in determining bank capital structures. Gropp and Florian (2007) concluded that there was a strong correlation between these variables which he referred to as the standard determinants of capital structure, on bank capital. For example, their study showed that profitable banks tend to have relatively more equity as their profit record makes their debt instruments very attractive while the tangibility of their assets also had a positive relationship with their debt ratio as these assets could be easily collateralized in line with the findings of Rajan and Zingales (1995).

2.4            Financial Leverage

Having examined the broader concept of capital structure, we now turn our focus to the narrower concept of financial leverage. Firms with huge portions of their capital structure comprised of debts are said to be highly levered while those with zero debts are referred to as being unlevered. Most publicly listed firms raise part of their long-term financial needs through the issuance of debt instruments like stocks or debentures or through a more direct debt instrument like borrowing. Borrowing, however, entitles lenders to receive some form of benefit sometimes in the form of interest, set at a predetermined rate to be redeemed on specified dates. Such interest on loans as observed by Tempel (2011) is equivalent to a charge on corporate profits that have to be satisfied before equity shareholders receive any benefits.

2.4.1         Financial Leverage Theories

As the trend towards financial leverage continues to grow over the years as noted by Buell (1981), financial researchers have advanced a number of theories to explain the growing embrace of leverages by finance chiefs. These theories include:

2.4.1.1      Trade-off Theory

Trade-off theory deals with financial distress and tax advantages of debt financing. Financial distress indicates a condition where promises to creditors are broken or honoured with difficulty ultimately leading to bankruptcy (Cassar & Holmes, 2003). The trade-off theory predicts that larger firms tend to be more diversified and hence likely to be less susceptible to financial distress. Further, if maintaining control is important, then it is likely that firms achieve larger size through debt rather than equity financing. Thus, control considerations also support a positive correlation between size and debt. Examining the effect of size in the determination of capital structure, Ferri and Jones (2009) found that larger firms are likely to use more debt. Therefore, a positive association is expected between firm’s size and leverage. Direct financial distress cost is inversely related to firm size (Cassar & Holmes, 2003). The ratio of direct bankruptcy costs to the value of the firm decreases as the value of firm increases. The impact of direct costs of bankruptcy on borrowing decisions of large firms is negligible.

According to Ang, Chua and McConnell (2012), larger firms are more diversified, have easier access to capital markets, and borrow at more favourable interest rates. Ang, Chua and McConnell (2012) argued that the large firms have lower agency costs associated with the asset substitution and under-investment problems, which mostly arise from the conflicting interests of shareholders and bondholders. Further, the smaller firms are more likely to be liquidated when they are in financial distress (Ozkan, 1996). Size is closely related to risk and bankruptcy costs. All such considerations suggest a positive relationship between the firm size, which is measured as the volume of total assets of firms, and the leverage ratio.

2.4.2.2      Agency Theory

Agency costs according to the Institute of Corporate Finance (2017) are “internal costs incurred due to the competing interests of shareholders (principals) and the management team (agents)”. Expenses that are associated with resolving this disagreement and managing the relationship are referred to as agency costs. As pointed out by the ICF (2017), “The key takeaway point is that these costs arise from separation of ownership and control. Shareholders want to maximize shareholder value while management sometimes makes decisions that are not in the best interest of shareholders (i.e. those that benefit themselves)”. Agency theory is concerned with these diverging interests between a firm’s owners and its management (Ang, Chua and McConnell, 2012). Ryen et al. (1997) theoretically summarized agency cost theory describing two sets of problems faced by firms; conflict between managers and stockholders and conflict between stockholders and bondholders. According to Ryen et al (1997), for the conflict between managers and stockholders, managers tend to either overspend or employ less leverage which is seen to be detrimental to shareholder interest. Managers would usually employ less leverage to minimize risk, which comprises of risks to their jobs, reputation, and wealth. On the other hand, overspending by managers tends to increase the opportunity cost of firms’ cash flow which could have been used on activities that benefit stockholders. The theory argues that there is a relationship between the agent (e.g. the manager), and the principal (e.g. the shareholders). The major assumption of this theory is that the separation of ownership and management creates conflicts among principals and agents. The emergence of the conflicts in the firm creates tension and result in high agency cost. It is assumed that the final objective of all stakeholders is to maximize their wealth. On the other side, agents may have other objectives rather than maximizing principals’ wealth. If the agents do not meet the principals’ interests and objectives, then conflict will arise among both parties (Jensen, 1986).

Jensen (1986) argued that the problem is how to motivate managers to distribute profits rather than investing it below the cost of capital and/or wasting it on organizational inefficiencies. One solution to this problem is to apply more debt in capital structure to compel managers to distribute profits (Jensen, 1986).

2.4.2.3 Static Trade-off Theory

The tax-adjusted Modigliani and Miller theory resulted in an incredible conclusion that firms should use only debt to maximize their value. The purpose of the trade-off theory is to explain why firms are financed partly by debt and partly by equity. The optimal capital structure of a firm is often explained as a trade-off between the cost and the benefits of debt. The optimal capital structure occurs when the marginal benefit and marginal cost of debt are equal. According to Jensen and Meckling (1976) cost in this theory is represented by the agency cost arising among creditors and owners and the cost of financial distress. Merit is measurable by the tax shield of debt (Myers, 1984). However, the optimal point differs from one firm to another due to the characteristics of each firm.

The optimized capital structure exists when the marginal cost of debt is equal to the marginal benefit of debt. If an unlevered firm starts to adjust its capital structure to include a small degree of leverage, this act will create a high benefit from interest tax shield without any huge increase in the distress cost. If the company increases its leverage more, the benefit would still be considerable but not as high as before. The cost of financial distress would also be high. If the rise in leverage increases, the cost of financial distress would exceed the tax shield benefit (Hillier, Jaffe, Jordan, Ross, and Westerfield, 2010).

2.4.2.4 Pecking Order Theory

Myers and Majluf (1984) gave this theory a rigorous theoretical foundation. According to Myers and Majluf, the theory advocates for an order in the choice of finance due to different degrees of asymmetry and agency costs present in various sources of finance. Accordingly, retained earnings are considered first in the financing pecking order because they are cheaper and are rarely affected by asymmetry of information. Second, debt is considered next since it carries low asymmetry which serves as a monitoring device against wasteful spending by the management. Finally, external equity is used as a last option because of its adverse selection effect. The model also asserts that outside investors can rationally discount the firm’s stock price when managers issue equity instead of risk less debt. This is because of the perception that a firm only issues equity when in financial trouble. In order to avoid this discount, managers avoid issuance of equity as much as possible. The implication of the pecking order approach is that firms do not have a target level of leverage and their actual level of debt essentially responds to the difference between investment and retained earnings (Benito, 2003).

2.4.2.5 Signaling Theory

This signalling theory asserts that financial decisions made by firms are signals to potential investors meant to compensate for information asymmetry. These signals, therefore, enable investors to make informed decisions concerning company investment. Ross (1977) linked the notion of signalling to capital structure theory and argued that since management have information on the correct distribution of the firm’s returns while outsiders do not, the firm is likely to benefit if the firm’s securities are overvalued. The reverse is also true as firms would suffer losses if their securities are undervalued. Roy (1977) also argued that managers can use higher financial leverage to signal an optimistic future for the company since debt capital involves a contractual commitment to pay back both principal and interest and failure to do so could result in bankruptcy which may further result to job losses. Hence, additional debt in the firm’s capital structure may be interpreted as a positive signal about a firm’s future.

  1. 4.2 Determinants of Financial LeverageProfitability

As explained by Titman and Wessels (2008), firms with the ability to generate an acceptable amount of profit and earnings levels tend to use their own internal source of funds to finance their projects. Therefore, it can be concluded that there is a negative relationship between firm profitability and the level of leverage. This conclusion is compatible with the ‘pecking order theory’ as well as other studies like that by Cassar and Holmes (2003) who reached the conclusion that firms generating adequate profits tend to leverage less by examining a sample of 20 financial firms in the United States.

  1. Firm size

The size of a firm has been viewed as an important feature which determines leverage. According to Bauer, the effect of size on leverage of corporate entities is ambiguous. Rajan and Zingales (1995) also pointed out that the existence of a positive relationship between firm size and leverage. While larger firms are generally less prone to bankruptcies as a result of their diversification, the reverse is true for smaller firms (Rajan and Zingales, 1995). Size, therefore, should have a positive impact on debt supply. As a consequence concludes Rajan and Zingales (1995:46), “larger firms tend to use debt while smaller ones are more likely to use equity in their respective finances”.

  • Growth Opportunities

Growth opportunities are firm investments or projects that have the potential to expand a firm’s capacity significantly, leading to higher profits (Wessels, 2008). Increases in the rate of growth of firms are generally seen as an indication of the firm’s financial strength. Researchers like Marsh (1982) have long observed that growing firms place a higher demand on internal reserves, while most firms with high growth often have high debt ratios. Titman and Wessels (2008) argue that there is a negative relationship between debt and a firm’s growth opportunities. According to the researchers, growth opportunities are equivalent to possessing capital assets which add value to firms, but since these do not generate current income, they, therefore, cannot be used as collateral. Therefore, firms with high growth opportunities tend to have lower leverage and vice versa.

  1. Non-debt tax shield

Bauer (2004) in his study conducted among firms in the Czech Republic discovered that apart from interest expenses, other items contribute to decreases in tax payments. These, he called non-debt tax shields like taxes deducted for depreciation. According to Bauer (2004), “Ceteris paribus” decreases in allowable investment-related tax shields (e.g., depreciation deductions or investment tax credits) due to changes in the corporate tax code or due to changes in inflation which reduce the real value of tax shields will increase the amount of debt that firms employ. Kim and Sorenson (1986:12) in their study observed: “there is a negative relationship between depreciation and capital structure which is consistent with the notion that depreciation is an effective tax shield, and thus offsets the tax shield benefits of leverage”. Bauer (2004) on the other hand, observed a positive relationship between firm leverages and non-debt tax shields.

  1. Liquidity

According to a study by Zingales and Rajan (1995), there’s a statistical relationship between liquidity and leverage. Liquidity, aside from indicating firm’s ability to meet current liabilities, also represents amounts available for use for day to day expenses and investment projects (Ross, 1977). Myers (1984) concluded that excessive amounts of current assets owned by firms increase their chances of internal funding which ultimately results in the relationship between leverage and equity.

 

2.5            Empirical Review of the Leverage – Investment Nexus

Over the years, a number of studies have been carried out with the aim of empirically assessing the relationship between financial leverage and investments. In this section, we examine some of these studies and highlight some of their findings and conclusions. The following paragraph gives an overview of recent studies of the leverage-investment relationship. We first examine studies carried out on leverage and investment before examining empirical studies on factors influencing the leverage-investment relationship.

  • Leverage and investment

Lang, Ofek, and Stulz, (1996) were one of the first authors to empirically examine the relationship between leverage and investment while controlling for growth opportunities at the firm level. Using a basic investment regression model and a sample comprising firms from the United States, the authors found that there is a negative relationship between leverage and investment-led growth at the firm level and, for diversified firms, at the segment level.Lang et al. (1996) concluded that the negative relationship was due to agency problems as management were mostly under pressure to avoid defaults in debt payments leading to significant declines in investment. According to the researchers, the negative relationship between leverage and investment is stronger for firms with low growth opportunities.

Two different studies by Aivazian, Ge, and Qui. (2005) conducted for Canadian firms and Odit & Chittoo (2008) for firms in Mauritius replicated the study by Lang, Ofek, Stulz, (1996) and concluded that leverage is mostly negatively related to investment. Their study found that the effect of leverage on investment was significantly greater for firms with low growth opportunities. Both studies pointed out the existence of agency problems and also indicated that debt often served as a protection mechanism against identified agency problems. Odit & Chittoo (2008) observed that with leveraging, management make a stronger case for not sharing profit as such funds are retained to pressing debt service needs. The studies were however criticized by Shadish et al. on the grounds that they lacked specificity and were actually biased towards construct validity.

Zhang (2009) carried out his study among Chinese firms and found a negative relationship between leverage and investment. According to Zhang (2009), among firms characterized by low growth opportunities, the relationship between leverage and investment was found to be quite strong. In contrast to previous studies conducted by Aivazian, Ge, Qui, (2005) and Odit & Chittoo (2008), Zhang employed residual analysis to determine ‘abnormal’ investment levels in order to analyze the extent leverage-investment relationship was caused by agency problems. Both the residual analysis and the analysis of the relation of leverage and investment empirically showed that debt served as a protection mechanism guarding against overinvestment. In the sample of firms with low growth opportunities, the leverage-investment relationship was found to be negative while the residuals showed a relatively low level of debt. Zhang concluded that agency problems were more severe in the sample of firms with low growth opportunities.

2.5.2         Factors Influencing the Leverage-Investment Relationship

Although Aivazian et al. (2005), Odit & Chittoo (2008) and Zhang et al. (2009) concluded that the negative leverage-investment relationship was down to agency problems, they neither mentioned nor empirically tested the role of net working capital in the relationship. Fazzari and Petersen (1993) bridged this gap in knowledge by examining the influence of Net working capital on the leverage-investment relationship and the agency problems. Their study highlighted evidence of firms smoothing fixed investment in the short run with working capital. According to the researchers, “it is costly for firms to change the level of fixed investments and therefore, firms seek another way to change investment spending by funding investments internally”. This finding is backed by De Gryse & De Jong (2006) who found that financially constrained firms with low growth opportunities reduced their working capital to smooth fixed investments when access to the external market is difficult. A firm’s reduction of net working capital in order to smoothen investments implies a negative relationship between leverage and investment. As investment increases, both leverage and net working capital decrease as management draw down on accumulated working capital (to fund investments) which in turn reduces the need for leveraging-funded investments. Hovakimian & Hovakimian (2007) found empirical evidence that when companies are characterized by high growth opportunities, managers build up financial reserves and increase net working capital in anticipation of financial constraints in the future. According to Hovakimian & Hovakimian (2007), management anticipating on the future by building up financial reserves also implies a negative leverage-investment relationship, which could affect and (partially) explain the relationship between leverage and investment as both net working capital and leverage increase while investment decreases.

Other factors have also been identified as influencing the relationship between leverage and debt. Pawlina (2010) investigated the underinvestment channel and described underinvestment “as an agency problem between shareholders and debt holders where a leveraged company foregoes valuable investment opportunities because debt holders would capture a portion of the benefits of the project, leaving insufficient returns to shareholders”. According to Pawlina (2010), underinvestment is exacerbated when debt is renegotiable in a period of financial distress with the firm expanding. This causes a higher wealth transfer from shareholders to creditors as the cost of external capital can decrease. But one has to consider that debt is not always negotiable and costs of increasing or decreasing leverage might be too high for the firm (O’Leary & Roberts, 2005).

This study sets out to contribute both to the empirical and theoretical literature on the nexus between investments and leverage by investigating the impact of financial leveraging on both financial and non-financial firms in the UK listed in the LSE. Our study adds a set of control variables which include Cash flow, Liquidity, Profitability and Sales Growth in order to measure the effect of leverage on investments among firms listed in the LSE. The following chapter introduces our methodology followed by the presentation and analysis of our data.

REFERENCES

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Ahmadpoor, A., Kashani-Poor, M., Shojai, M.R., (2011). The effect of corporate governance and audit quality on the cost of debt financing (borrowing). Journal of Accounting and Auditing Review, (17) 62, 32-17.

Aivazian, V.A., Ge, Y., & Qui, J. (2005). The impact of leverage on firm investment: Canadian Evidence, Journal of Corporate Finance, 11, 277-291

Ang, J. S., Chua, J. H., & McConnell, J. J. (2012), “The Administrative Costs of Corporate Bankruptcy. The Journal of Finance, 37( 2), 219-226.

Bae, K. H., and Goyal, V. K. (2010). Equity market liberalization and corporate governance. Journal of Corporate Finance, (16) 609-621.

Bevan, A. A., Danbolt, J. (2001), On the Determinants and Dynamics of UK Capital Structure, Working Paper Series, Department of Accounting & Finance University of Glasgow,(11) 1-37.

Brealey, R. & Myers, C. (1991). Principles of Corporate Finance. 4th ed. New York McGraw-Hill, U.S.A.

Botta, M. (1989) Firm Financing in the Euro Area: How Asset Risk Affects Capital Structure. London, Academic Press.Cassar, G., & Holmes, S. (2003). Capital structure and financing of SMEs: Australian evidence. Journal of Accounting and Finance, 43(2), 123–47

Chiang, Y. C., & Ko, C. L. (2009). An empirical study of equity agency costs and internationalization: Evidence from Taiwanese firms. Research in International Business and Finance, (23), 369-382.

Corporate Institute of Finance (2017) Agency Costs. The Cost Shareholder Bare for Having a Manager Run their Business.

Fama, E. & French, R. (2002). Testing the Tradeoff and Pecking Order Predictions about Dividends and Debt. Review of Financial Studies, (15), 1-33.

Fazzari, S.M., Hubbard, R.G., Petersen, B.C., Blinder, A.S., Poterba, J.M. (1988). Financial constraints and corporate investment, Brooking Papers in Economic Activity, Vol. 1, No. 1, pp. 141-206

Ferri, M. G., & Jones, W. H. (2009). Determinants of financial structure: A new methodological approach, Journal of Finance, 34(3), 631–644

Gropp, R. & Florian, H. (2007). What can corporate finance say about banks’ capital structures? Journal of Finance (21), 1-31.

Hillier, D., Jaffe, J., Jordan, B., Ross, S., and Westerfield, R. (2010). Corporate Finance, First European Edition, McGraw-Hill Education

Hovakimian, A., Hovakimian, G. (2009). Cash flow sensitivity of Investment, European Financial Management, 15(1), 47-65

Jensen, M., & Meckling, W. (1976), “Theory of the Firm: Managerial Behaviour, Agency Costs and Ownership Structure”, Journal of Financial Economics, 3(4), 305-360

Jensen, M.C. (1986). Agency costs of free cash flow, corporate finance and takeovers, American Economic Review, vol. 76, no.2, pp. 323-329

William H. Meckling (1976), “Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure”. Journal of Financial Economics, Vol. 3, No. 4, pp. 305-360.

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CHAPTER THREE

RESEARCH METHODOLOGY

3.1       Introduction

This study which aims to investigate the effects of financial leverage on financial and non-financial firms in the UK adopts the methodology employed by Sajid et al. (2016) in their study which examined the influence of financial leverage on investment decisions in Pakistan using a sample of 30 (financial and non-financial) companies listed in the Karachi Stock Exchange (KSE). A five-year dataset of relevant variables was employed for the companies covering from 2009 to 2013. The researchers utilized descriptive statistics correlation and pooled Ordinary Least Square (OLS) techniques in their analyses.

For our study, however, we take some steps further by investigating both unit root and cointegration tests for panel dataset obtained before estimation. This is to improve the robustness of the estimation procedures. All our analysis are done using the software eviews 7.0.

In the next section, we present the empirical model for this study followed by another section on the estimation techniques and procedures utilised for the study. The last section discusses the sources of data and measurement of the variables used for the study.

3.2       Model Specification

Drawing from the empirical model by Sajid, Mahmood and Sabir (2016), with a slight modification, the model of our study is stated in its functional form as;

I = f (LEV + CF + LIQ + PFT + SALES) …,                                                            (1)

Equation (1) above can be restated in econometric form as

Ii,t = α + β1LEVi,t-1 + β2CFi,t-1 + β3LIQi,t-1 + β4PFTi,t-1 + β5SALESi,t-1 + μi,t….                        (2)

Where

It represents the investment of firm i during the period t;

α is a constant

β1, β2, β3, β4, and β5 symbolizes the coefficients of (Leverage, Cash Flow, Liquidity, Profitability, and Sales respectively);

LEVi,t-1 represents the lagged leverage;

CFi,t-1 is the lagged cash flow;

LIQi,t-1 is the lagged liquidity;

PFTi,t-1 is the lagged profitability;

SALESi,t-1 stands for lagged net sales of firm i

i is the observation of the ith firm

t is the time period

μt is the error term

 

3.3       Estimation Techniques

3.3.1    Pooled Ordinary Least Squares (POLS)

Panel data ordinary least square technique is a modified form of the Ordinary Least Square (OLS) technique. It is simply the use of OLS approach in estimating time series data across sections i.e., panel data. This technique can be performed in three ways i.e. random effect, fixed effect and common constant approach (Asteriou and Hall, 2007).

The common constant approach also known as the pooled OLS method is adopted for analysis of this study. It is based on an assumption that there are no differences among the data matrices of cross-sectional dimensions of firms (Asteriou and Hall, 2007). Hence, the model estimates a common constant for all cross-sections.

However, this is useful under the hypothesis that the dataset is homogenous i.e., a sample of firms that operate in the same stock market. This is expressed in equation 3 below as;

…                                                                        (3)

Where Y (dependent) and x (independent) variables have sub-scripts i for the ith firm and t for time periods. Uit is the error term associated with the ith firm at period t.

3.3.3    Pre-Estimation Techniques

Some pre-estimation tests were conducted before estimating the POLS. This is to ensure that the estimated coefficients are reliable and robust results are obtained. The pre-estimation techniques carried out include; descriptive statistics, correlation, panel unit root and cointegration tests.

Unit root test was performed using the common unit root process which assumes a common unit root associated with the panel data. The common unit root process adopted in this study is the Levin, Lin and Chu (LLC) test which considers the basic ADF specification. It is performed under a null hypothesis that there is unit root against an alternative that there is no unit root.

Lastly, the cointegration test utilised in this study is the Kao test based on the Engle-Granger two-step residual based test.

The residual based DF and ADF Kao test is given as:

Where  and  are I(1).

Kao (1999) proposed DF and ADF type unit root tests for  as a test for the null hypothesis of no cointegration for all i. Clearly, it is an extension of the Engle-Granger cointegration framework tests.

3.3.4    Estimating the Investment / Leverage and Financial / Non-Financial Sectors Relationships

In order to analyze the impact of Leverage on Investment, our study adopts investment as the dependent variable and Leverage as an independent variable. Our study also sets out to analyze the impact of Leveraging on Investment in both the financial and non-financial sectors of companies listed in the LSE. Therefore, two sets of estimates will be made highlighting the results for the relationship between Investment and Leveraging in the financial and non-financial sectors.

In assessing the impact of Leverage on Investment, we observe that the relationship between both variables could be significantly impacted by other external variables. It was therefore expedient that we introduced other control variables into our equation i.e., Profitability, Cashflow, Liquidity and Sales into the model as control variables. In order to estimate the unique effects of Leverage on Investment, the pooled OLS estimates will be categorized into sections highlight the different results; without control variables and with control variables.

3.4       Data Sources and Measurement Technique

3.4.1    Sources of Data and Sampling Technique

Our data and variables are calculated from the Annual reports of selected listed companies on the London Stock Exchange. The annual reports are downloaded from the financial reports data site, www.annualreports.com. Data for 20 selected firms were gathered for the five year period from 2011 to 2015.

The study employs a 2-stage stratified random sampling technique in drawing a sample of firms whose annual reports were employed for the study. The sample firms are first divided into 6 strata representing 6 sectors listed in the LSE i.e., Financial, Healthcare, Basic Materials, Services, Consumer Goods, and Technology. From each stratum, companies are then drawn based on the availability of annual reports on the website. A total of 20 companies across 6 sectors were sampled. The companies with their respective sectors are listed below:

Financial sector        

1          British Empire Securit. & Gen. Trust plc

2          Aberdeen Asset Management PLC

3          3i Group plc

4          Ashmore Group PLC

Healthcare Sector    

5          Dechra Pharmaceuticals plc (000)

6          GlaxoSmithKline PLC

7          Hikma Pharmaceuticals PLC

Basic Materials        

8          Anadarko Petroleum Corporation

9          Evraz

10        Ferrexpo PLC

11        Acacia Mining PLC

Services         

12        Bunzl PLC

13        Hargreaves Services PLC

14        Acal PLC

Consumer Goods

15        DS Smith PLC

16        RPC Group PLC

17        McBride PLC

Technology

18        Berendsen PLC

19        Fidessa Group PLC

20        RM PLC

3.4.2    Data Description and Measurement

Following Sajid, Mahmood and Sabir (2016), and Odit and Chittoo (2008) the following variables extracted from the company balance sheets and employed for the study.

  1. a) Investment: Investment is a commitment to one or more assets which will be held for some future time period with some expectations of future gain. From an investment point of view, risk means that the actual gains from the investment will be different from expected outcomes.
  2. b) Financial Leverage: Financial leverage measure used in this study is the ratio of the book value of long-term debt to total assets. This measure is adopted primarily because it does not reflect recent changes in the company’s market valuation.
  3. c) Liquidity Ratio: This ratio shows the ability of a firm’s short-term assets (cash, inventory, receivables) to pay its short-term debt. As the current ratio will be higher, the firm will be more capable of paying its short-term obligations. If the ratio is below 1, then it indicates that the firm is unable to pay off its short-term liabilities and the firm is considered to be financially weak but not necessarily bankrupt. Since companies differ across industry, it is always useful to compare companies in the same industry.
  4. d) Net Fixed Assets: Net Fixed Assets refer to land, buildings, and equipment which have more than one year life and were acquired to carry on the business of a company. In financial records, the Net Fixed Assets is the purchase price of all fixed assets less accumulated depreciation, i.e. effective property, plant and equipment after depreciation.
  5. e) Profitability: The profit margin is commonly used for internal comparison. It is an indicator of the pricing strategies of the company and how well cost is under control. The differences in competitive strategy and product mix cause profit margin to differ between companies.

Profitability is measured in terms of the relationship between net profits and assets. It shows the operating efficiency of the total funds over investments of a firm.

  1. f) Sales Growth: Sales growth shows the performance of a firm with respect to the previous year’s performance. The increase in sales over a specific period of time is measured by the sales growth.
  2. g) Cash Flow: The cash flow is measured as the total of earnings before extraordinary items and depreciation. The same measure of cash flow is used by Odit and Chittoo (2008) in their study.

The various variables are measured as below:

Table 3.1        Variable Measurements

  Variables Measurement
1 Investment Balance sheet figure in millions
2 Financial Leverage Long term Debt / Total Assets
3 Liquidity Current Assets / Current Liabilities
4 Asset Efficiency Net Fixed Assets
5 Profitability (Net profit after taxes / Total fixed Assets)×100
6 Sales Growth Net Sales / Net Fixed Assets
7 Cash Flow Total of earnings before extraordinary items & depreciation
     

 

REFERENCES

Asteriou, D. and Hall, S. (2007) Applied Econometrics, A Modern Approach. Palgrave Macmillan, New York.

Odit, M.P. and H.B. Chittoo, 2008. Does financial leverage influence investment decisions? The case of Mauritian firms. Journal of Business Case Studies, 4(9): 49-60.

Sajid, M., Mahmood .A, and Sabir H. (2016), Does Financial Leverage Influence Investment Decisions? Empirical Evidence from Kse-30 Index of Pakistan. Asian Journal of Economic Modelling. 4(2): 82-89

This chapter presents the results of all the estimations conducted. The first section provides results of the preliminary results and tests namely: descriptive statistics, correlation matrix, Levin, Lin and Chu (LLC) unit root and Kao residual-based cointegration test. In the second section, we have the estimated results of the pooled OLS analyses while the third section assesses the hypotheses of the study and implication of findings.

4.1 Preliminary Results.

Table 4.1 Descriptive Statistics of Variables

  INV LEV CF LIQ PFT SALES
 Mean  24115.17  0.281253  3603.224  2.119542  1.473814  0.854732
 Median  2290.500  0.277104  190.7500  1.456587  0.096742  0.710773
 Maximum  415126.0  0.748311  195756.0  9.808824  108.2809  2.257143
 Minimum  112.7000  0.003687 -11706  0.612821 -0.7395  0.021226
 Std. Dev.  74734.50  0.192266  20329.42  1.589621  11.06846  0.612859
 Skewness  4.120892  0.403653  8.652443  2.164947  9.214417  0.565231
 Kurtosis  19.30215  2.475699  81.56581  8.367285  88.66657  2.230048
 Jarque-Bera  1390.363  3.860981  26966.85  198.1488  31993.26  7.794886
 Probability  0.000000  0.145077  0.000000  0.000000  0.000000  0.020294
 Observations 100 100 100 100 100 100
 Cross sections 20 20 20 20 20 20

Source: Author’s Computation Using eviews

On Table 4.1 above is the descriptive statistics of the variables considered in the study. On the average, INVESTMENT, LEVERAGE and CASHFLOW had values 24115.17, 0.281253 and 3603.22 respectively. The other values 2.119542, 1.473814 and 0.854732 are the statistical means of LIQUIDITY, PROFITABILITY and SALES respectively. The table shows that INVESTMENT ranged from 112.7 to 415126 while LEVERAGE ranged between 0.003887 and 0.748311 for the period considered in the London Stock Exchange (LSE). Standard deviation figures indicate that INVESTMENT (74734.50) fluctuated most while LEVERAGE (0.192266) fluctuated the least among the variables. All the estimated values of skewness are positive and it shows that all the variables are skewed to the right. On the other hand, the values of the kurtosis imply that the variables are clustered or ‘leptokurtic’ in their distribution.  The probability values of the Jacque-Bera statistics were all significant except that of LEVERAGE. It means that of all variables employed for our study, only LEVERAGE is normally distributed among the variables.

Table 4.2 Correlation Matrix of Variables.

 Variables INV LEV CF LIQ PFT SALES
INV  1.000000  0.006042  0.609253 -0.06465  0.510966 -0.10147
LEV  0.006042  1.000000 -0.03082 -0.16066 -0.08296  0.070532
CF  0.609253 -0.03082  1.000000 -0.00794  0.120379 -0.12437
LIQ -0.06465 -0.16066 -0.00794  1.000000 -0.02843 -0.31258
PFT  0.510966 -0.08296  0.120379 -0.02843  1.000000 -0.03241
SALES -0.10147  0.070532 -0.12437 -0.31258 -0.03241  1.000000

Source: Author’s computation using eviews

The correlation matrix of variables is displayed in Table 4.2 above. It shows that INV is positively correlated with LEVERAGE, CASHFLOW and PROFITABILITY while it is negatively related to LIQUIDITY and SALES. Also from the table, LEVERAGE is negatively associated with CASHFLOW, LIQUIDITY and PROFITABILITY but moves in the same direction with SALES. These relationships for the most part, follow à priori expectations. None of the correlation coefficients is up to 0.9 in absolute terms indicating that the independent variables used are not threatened by multi-collinearity problem.  This means that estimating a regression with these variables would not yield spurious results.

Table 4.3 LLC Unit Root Test.

Variable t-Statistics Remark
INV -7.96501*** I(0)
LEV -16.5079*** I(1)
CF -386.594*** I(0)
LIQ -3.18062*** I(0)
PFT -98.5404*** I(1)
SALES -3.30797*** I(0)

Note: *** connotes significance at 1% level

Source: Author’s computation using eviews

The LLC unit root results are presented in Table 4.3. All the LLC t-statistics are significant at the 1 percent level implying a rejection of the null hypothesis of a common unit root in the variables. The result shows that the variables INVESTMENT (INV), CASHFLOW (CF), LIQUIDITY (LIQ) and SALES are stationary at levels   (integrated of order zero), while LEVERAGE (LEV) and PROFITABILITY (PFT) are stationary after first difference (integrated of order one). Hence the variables are integrated of different orders which necessitates the use of a residual-based cointegration test among the variables.

Table 4.4 Kao Residual-Based Cointegration Result

Variable ADF t-Statistics Remark
Residual -12.2988*** I(0)

Note: *** connotes significance at 1% level                                                                           Source: Author’s computation using eviews 7.0

On Table 4.4 is the Kao residual-based cointegration result. The estimated ADF t-statistic is significant at the 1 percent level indicating a rejection of the null hypothesis of no cointegration. This implies that the residual series are stationary at level [integrated of order zero, I (0)], hence there is the existence of a long run relationship or cointegration among the variables. The use of the pooled OLS technique is hereby appropriate for our analysis.

 

4.2 Model Estimates

Table 4.5 Estimates of the Pooled OLS.

Dependent Variable: INV

  All Sectors

 

Financial Sector

 

Non-Financial Sector

 

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Constant 23454.6* 15406.53 2200.924*** 995.3189 39546.35* 31307.39
LEV 2348.678 21233.98 3705.334* 2987.386** -31946.3 6159.236
CF   2.029503***   7.178418*   1.966683***
LIQ   -2383.67   -44.5071   -2743.51
PFT   3013.877***   -4552.53**   2950.909***
SALES   -4639.85   6913.542**   -12252
R-Squared 0.000037 0.571897 0.195222 0.833704 0.004064 0.570117
F-Stat 0.003578 25.11468 4.366422 14.03749 0.318285 19.62796
Prob F-Stat 0.952423 0 0.051121 0.000049 0.574259 0
DW 0.072538 1.965192 1.746539 1.514123 1.806206 1.77869

Note: *, ** and *** connotes significance at 10%, 5% and 1% levels respectively.                Source: Author’s computation using eviews 7.0

Table 4.5 provides the compiled estimated models of the pooled OLS. For ease of analysis and in order to determine the effect of Leverage alone on Investment, the pooled OLS estimates have been categorized into sections showing results for all sectors combined, the financial sector and the non-financial sectors as well as a demarcation drawn between estimated results with control variables, and those without control variables. They are presented in columns 2, 4 and 6 respectively while on columns 3, 5 and 7 are estimates of the models with control variables for all sectors, financial sector, and those for the non-financial sectors respectively.

The models without control variables (Models 1, 3 and 5) show that while LEVERAGE had a significant impact on INVESTMENT among firms in the financial sector, it had no significant impact on INVESTMENT among firms in both the non-financial sector and when both sectors are combined.  The very low R-Squared values associated with models 1, 3 and 5 tells us that LEVERAGE alone is not enough to explain the dynamics of firm INVESTMENT thus, underlining the relevance of our control variables in the analysis.

With control variables where CASHFLOW, LIQUIDITY, PROFITABILITY and SALES were included in the estimates of the different categories, we have more robust results as shown by Models 2, 4 and 6 on the table. The relatively high R-squared values indicate that at least 57 percent of the fluctuations in INVESTMENT are explained by the models. Their f-statistic values (25.11468, 14.03749 and 19.62796) were all significant at the 1 percent level implying that the independent variables jointly explained variations in INVESTMENT. More so, there is no threat of serial correlation among residual terms of the models judging from the Durbin-Watson values of 1.97, 1.51 and 1.78 for Models 2, 4 and 6 respectively. Thus, the estimated models with control variables are valid for meaningful analysis.

Focusing on models 2, 4 and 6, which contain control variables, the estimated result show that LEVERAGE positively impacts INVESTMENT, but its impact was significant only in the financial sector. It implies that increases in LEVERAGE would boost INVESTMENT levels of firms, in the financial sector. CASHFLOW promotes INVESTMENT with significant impacts in all sectors and non-financial sector. However, it has no significant impact on INVESTMENT in the financial sector.  On another hand, LIQUIDITY had a negative influence on INVESTMENT. Its relationship with INVESTMENT is not significant in all the models considered.  PROFITABILITYFT had a significant effect on INVESTMENT in the models but with mixed results. Its relationship with INVESTMENT was positive in all sectors and non-financial sector while it had a negative relationship with INVESTMENT in the financial sector. SALES also had a mixed result in the analysis. While it has a positive and significant effect on INVESTMENT in the financial sector, it had a negative and insignificant effect on the non-financial sector and on all the sectors combined.

4.3 Summary of Findings, Implication of Findings and Assessment of Hypotheses.

: Financial leveraging has no significant impact on investment among firms listed in the London Stock Exchange

Our model result indicates that without control variables, Financial Leveraging alone had no significant impact on Investment. With the introduction of control variables in our model, the relationship was yet insignificant in all firms. We therefore reject the null hypothesis and accept the alternative hypothesis while concluding that financial leverage has no significant impact on investment among firms in the UK.

: Financial Leveraging has no significant impact on financial firms listed in the London Stock Exchange

Our model result without control variables shows that while financial leverage had a positive relationship with investment, the very low R2 indicates that financial leverage alone is not enough to measure variations in investment. With the introduction of control variables (Cashflow, Liquidity, Sales, and Profitability), the relationship stayed positive and was significant with the model accounting for 57% of variations in Investment. We therefore accept the null hypothesis and conclude that financial leveraging has a significant impact among financial firms listed in the London Stock Exchange.

: Financial leveraging has no significant impact on investment among non-financial firms listed in the London Stock Exchange

Our model result used to test this hypothesis shows that without the introduction of control variables, financial leveraging had a negative and non-significant effect with investment among non-financial firms. With the introduction of control variables, though the relationship was positive, it was insignificant. We therefore reject the null hypothesis and accept the alternative hypothesis while concluding that financial leveraging has no significant impact on investment among non-financial firms listed in the London stock exchange.

CHAPTER FIVE

SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.1       Summary

This study examined the impact of financial leverage on both financial and non-financial firms in the UK by examining a sample of 20 firms listed in the London Stock Exchange. For the purpose of conducting an empirical study, we collected data from a sample of 20 financial and non-financial firms listed in the LSE and extracted data from their audited annual financial accounts over a five year period (2011 – 2015). We applied descriptive statistics, correlation analysis and pooled ordinary least square regression model as well as the Levin, Lin and Chu (LLC) unit root and Kao residual-based cointegration tests. The regression results of the OLS model indicated that financial leverage had no impact on Investment among firms listed in the London Stock exchange. When disaggregated however, our results showed that financial leveraging had a positive and significant impact on investment among financial firms listed in the LSE. On the other hand, our model also showed that Financial Leveraging had a positive but insignificant impact with investment among non-financial firms listed in the London Stock Exchange.

The significance of our control variables was underlined by the adjusted coefficient of determination R2 which ranged from 57% – 83% in all models containing our control variables. This indicates that, our control variables contributed to at least 57% of the systematic variation in the dependent variable (investment).

5.2       Conclusion

This study concludes that while financial leverage is a key determining variable for investment among firms operating in the financial sector in the UK, it is not that much of an important variable among firms operating in the non-financial sector when it comes to investment. This suggests that while firms operating in the financial sector embrace financial leveraging as a critical investment tool, firms operating in the non-financial sector look towards other variables like profit, sales, liquidity and cash flow to determine their level of investment.

5.3       Recommendations for Further Research

1) Owing to time constraints, our study was unable to look into the features and characteristics of firms in both the financial and non-financial sectors in the UK in order to understand their investment paths. Further research can be carried out in this area in order to shed more light on some of our results.

2) Our study attempted to cater for the various sectors contained in the LSE which therefore resulted in a disproportionate representation of firms in both the financial and non-financial sectors of our samples. Further research could be carried out in this area with equal representation between sectors.

3) While our study highlight the differences in leverage adopted by the financial and non-financial sectors, it does not explain the likely reasons these classes of firms seem to have chosen divergent leveraging paths. This area is worth looking into.

 

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APPENDICES

Descriptive Statistics All Sectors

  INV   LEV CF LIQ PFT SAL
 Mean  24115.17    0.281253  3603.224  2.119542  1.473814  0.854732
 Median  2290.500    0.277104  190.7500  1.456587  0.096742  0.710773
 Maximum  415126.0    0.748311  195756.0  9.808824  108.2809  2.257143
 Minimum  112.7000    0.003687 -11706.00  0.612821 -0.739497  0.021226
 Std. Dev.  74734.50    0.192266  20329.42  1.589621  11.06846  0.612859
 Skewness  4.120892    0.403653  8.652443  2.164947  9.214417  0.565231
 Kurtosis  19.30215    2.475699  81.56581  8.367285  88.66657  2.230048
               
 Jarque-Bera  1390.363    3.860981  26966.85  198.1488  31993.26  7.794886
 Probability  0.000000    0.145077  0.000000  0.000000  0.000000  0.020294
               
 Sum  2411517.    28.12525  360322.5  211.9542  147.3814  85.47319
 Sum Sq. Dev.  5.53E+11    3.659639  4.09E+10  250.1626  12128.57  37.18400
               
 Observations 100   100 100 100 100 100
 Cross sections 20   20 20 20 20 20

Correlation Matrix All Sectors

  INV LEV CF LIQ PFT SALES
INV  1.000000  0.006042  0.609253 -0.064653  0.510966 -0.101472
LEV  0.006042  1.000000 -0.030821 -0.160657 -0.082963  0.070532
CF  0.609253 -0.030821  1.000000 -0.007936  0.120379 -0.124365
LIQ -0.064653 -0.160657 -0.007936  1.000000 -0.028425 -0.312584
PFT  0.510966 -0.082963  0.120379 -0.028425  1.000000 -0.032410
SALES -0.101472  0.070532 -0.124365 -0.312584 -0.032410  1.000000

Unit Root Test

Null Hypothesis: Unit root (common unit root process)  
Series: INV_01, INV_02, INV_03, INV_04, INV_05, INV_06, INV_07, INV_08,
        INV_09, INV_10, INV_11, INV_12, INV_13, INV_14, INV_15, INV_16,
        INV_17, INV_18, INV_19, INV_20      
Date: 03/22/18   Time: 14:11        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 80        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -7.96501    0.0000  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on INV_?        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
INV_01  1.60264  5.E+06  6.E+06  0  0  0.0  4
INV_02  0.08746  512001  653467  0  0  0.0  4
INV_03  0.14605  125253  673422  0  0  1.0  4
INV_04  0.03548  2425.3  3137.9  0  0  0.0  4
INV_05 -0.01902  8.E+09  8.E+09  0  0  0.0  4
INV_06  0.07338  3.E+07  4.E+07  0  0  0.0  4
INV_07  0.10151  29752.  107181  0  0  1.0  4
INV_08 -0.03170  6.E+07  5.E+07  0  0  1.0  4
INV_09 -0.11832  7.E+06  1.E+07  0  0  1.0  4
INV_10 -0.11675  298102  548010  0  0  1.0  4
INV_11 -0.09154  153200  172875  0  0  1.0  4
INV_12  0.07240  5284.7  81684.  0  0  1.0  4
INV_13 -0.91761  85101.  7.E+06  0  0  0.0  4
INV_14 -0.99498  18649.  1.E+08  0  0  0.0  4
INV_15  0.07488  562105  608782  0  0  0.0  4
INV_16  0.27848  101150  166214  0  0  0.0  4
INV_17 -0.02698  276.37  418.00  0  0  0.0  4
INV_18 -0.00963  945.50  916.01  0  0  1.0  4
INV_19  0.01734  28.665  49.000  0  0  0.0  4
INV_20 -0.08569  15.172  257.00  0  0  1.0  4
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -0.46878 -8.355  14.602  0.004  1.049    80
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: LEV_01, LEV_02, LEV_03, LEV_04, LEV_05, LEV_06, LEV_07,
        LEV_08, LEV_09, LEV_10, LEV_11, LEV_12, LEV_13, LEV_14, LEV_15,
        LEV_16, LEV_17, LEV_18, LEV_19, LEV_20    
Date: 03/22/18   Time: 14:14        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 80        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -0.61523    0.2692  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on LEV_?        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
LEV_01  7.45566  0.1099  0.1335  0  0  0.0  4
LEV_02 -0.41144  0.0003  0.0005  0  0  0.0  4
LEV_03 -0.37051  0.0035  0.0477  0  0  1.0  4
LEV_04 -0.69122  5.E-06  0.0002  0  0  0.0  4
LEV_05 -0.37658  0.0258  0.0101  0  0  3.0  4
LEV_06  0.07762  0.0016  0.0031  0  0  0.0  4
LEV_07 -0.07723  0.0098  0.0078  0  0  1.0  4
LEV_08  0.01430  0.0036  0.0036  0  0  0.0  4
LEV_09  0.12399  0.0081  0.0169  0  0  1.0  4
LEV_10  0.11612  0.0072  0.0095  0  0  0.0  4
LEV_11  0.07795  7.E-05  0.0003  0  0  1.0  4
LEV_12 -0.00153  0.0014  0.0004  0  0  3.0  4
LEV_13 -0.25567  0.0086  0.0141  0  0  0.0  4
LEV_14 -0.59706  0.1373  0.0378  0  0  3.0  4
LEV_15 -0.09055  0.0145  0.0033  0  0  3.0  4
LEV_16  0.11761  0.0004  0.0028  0  0  1.0  4
LEV_17  0.12944  0.0018  0.0027  0  0  0.0  4
LEV_18 -0.08037  0.0012  0.0026  0  0  0.0  4
LEV_19 -0.02029  2.E-05  1.E-05  0  0  2.0  4
LEV_20 -0.11563  0.0042  0.0037  0  0  1.0  4
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -0.01595 -0.642  1.869  0.004  1.049    80
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: LEV_01, LEV_02, LEV_03, LEV_04, LEV_05, LEV_06, LEV_07,
        LEV_08, LEV_09, LEV_10, LEV_11, LEV_12, LEV_13, LEV_14, LEV_15,
        LEV_16, LEV_17, LEV_18, LEV_19, LEV_20    
Date: 03/22/18   Time: 14:20        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 60        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -16.5079    0.0000  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on D(LEV_?)        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
D(LEV_01)  1.45050  0.1778  0.1778  0  0  0.0  3
D(LEV_02) -1.00623  0.0001  0.0008  0  0  0.0  3
D(LEV_03) -0.50431  0.0054  0.0162  0  0  0.0  3
D(LEV_04) -0.77394  1.E-06  0.0001  0  0  0.0  3
D(LEV_05) -1.54721  0.0319  0.0334  0  0  2.0  3
D(LEV_06) -1.02470  0.0017  0.0018  0  0  2.0  3
D(LEV_07) -1.68597  0.0110  0.0231  0  0  0.0  3
D(LEV_08) -0.78973  0.0045  0.0049  0  0  0.0  3
D(LEV_09) -0.24788  0.0106  0.0111  0  0  0.0  3
D(LEV_10) -0.80967  0.0122  0.0072  0  0  2.0  3
D(LEV_11) -0.32233  5.E-05  7.E-05  0  0  0.0  3
D(LEV_12) -1.43866  0.0006  0.0023  0  0  2.0  3
D(LEV_13) -1.67567  0.0148  0.0229  0  0  0.0  3
D(LEV_14) -1.53554  0.0723  0.2815  0  0  2.0  3
D(LEV_15) -1.61498  0.0035  0.0219  0  0  2.0  3
D(LEV_16) -0.41547  0.0004  0.0006  0  0  1.0  3
D(LEV_17) -0.74338  0.0033  0.0023  0  0  2.0  3
D(LEV_18) -1.05516  0.0028  0.0015  0  0  2.0  3
D(LEV_19) -1.32664  3.E-05  2.E-05  0  0  2.0  3
D(LEV_20) -1.29059  0.0034  0.0104  0  0  1.0  3
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -0.80901 -17.304  1.269  0.004  1.049    60
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: CF_01, CF_02, CF_03, CF_04, CF_05, CF_06, CF_07, CF_08, CF_09,
        CF_10, CF_11, CF_12, CF_13, CF_14, CF_15, CF_16, CF_17, CF_18,
        CF_19, CF_20          
Date: 03/22/18   Time: 14:15        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 80        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -386.594    0.0000  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on CF_?        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
CF_01 -1.02636  2842.9  3833.1  0  0  3.0  4
CF_02  0.10568  2597.6  5610.9  0  0  1.0  4
CF_03  0.27087  59871.  65448.  0  0  0.0  4
CF_04 -0.06624  408.48  565.54  0  0  0.0  4
CF_05 -0.64941  9.E+09  3.E+09  0  0  3.0  4
CF_06 -0.04678  1.E+07  4.E+06  0  0  3.0  4
CF_07  0.17448  3857.7  8912.8  0  0  1.0  4
CF_08 -0.37268  1.E+07  1.E+07  0  0  0.0  4
CF_09 -0.57178  115525  418127  0  0  0.0  4
CF_10 -0.04702  14797.  14984.  0  0  0.0  4
CF_11 -1.29078  106250  113002  0  0  3.0  4
CF_12  0.02080  3203.0  2686.1  0  0  1.0  4
CF_13 -0.57009  723.11  3275.3  0  0  0.0  4
CF_14 -1.00785  23.935  3.E+07  0  0  0.0  4
CF_15  0.17485  1680.8  4235.1  0  0  1.0  4
CF_16  0.10019  436.30  353.39  0  0  3.0  4
CF_17 -0.27218  146.00  146.99  0  0  2.0  4
CF_18  0.01794  354.42  378.11  0  0  0.0  4
CF_19 -0.02478  8.8355  6.0000  0  0  2.0  4
CF_20 -0.07792  20.081  21.250  0  0  0.0  4
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -1.00738 -405.535  5.945  0.004  1.049    80
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: LIQ_01, LIQ_02, LIQ_03, LIQ_04, LIQ_05, LIQ_06, LIQ_07, LIQ_08,
        LIQ_09, LIQ_10, LIQ_11, LIQ_12, LIQ_13, LIQ_14, LIQ_15, LIQ_16,
        LIQ_17, LIQ_18, LIQ_19, LIQ_20      
Date: 03/22/18   Time: 14:17        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 80        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -3.18062    0.0007  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on LIQ_?        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
LIQ_01 -0.20035  5.5578  1.8681  0  0  3.0  4
LIQ_02  0.02159  0.0153  0.0076  0  0  3.0  4
LIQ_03  0.07640  1.1717  1.6511  0  0  1.0  4
LIQ_04  0.12720  0.1094  0.6218  0  0  1.0  4
LIQ_05  0.11597  0.0791  0.1251  0  0  0.0  4
LIQ_06  0.03568  0.0085  0.0085  0  0  2.0  4
LIQ_07  0.17592  0.5582  0.4966  0  0  1.0  4
LIQ_08 -0.09582  0.0686  0.0858  0  0  0.0  4
LIQ_09 -0.07321  0.0331  0.0606  0  0  1.0  4
LIQ_10 -0.34160  0.1605  8.4825  0  0  1.0  4
LIQ_11 -0.18335  0.0318  1.1503  0  0  1.0  4
LIQ_12  0.00086  0.0118  0.0042  0  0  2.0  4
LIQ_13 -0.21810  0.3587  0.5972  0  0  0.0  4
LIQ_14 -0.33672  0.6164  1.1862  0  0  0.0  4
LIQ_15 -0.15303  0.2110  0.1040  0  0  3.0  4
LIQ_16  0.04471  0.0116  0.0140  0  0  0.0  4
LIQ_17  0.03023  0.0036  0.0044  0  0  0.0  4
LIQ_18 -0.06311  0.0326  0.0174  0  0  3.0  4
LIQ_19  0.00488  0.0020  0.0021  0  0  0.0  4
LIQ_20 -0.33508  9.9685  11.983  0  0  0.0  4
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -0.05582 -3.334  1.862  0.004  1.049    80
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: PFT_01, PFT_02, PFT_03, PFT_04, PFT_05, PFT_06, PFT_07,
        PFT_08, PFT_09, PFT_10, PFT_11, PFT_12, PFT_13, PFT_14, PFT_15,
        PFT_16, PFT_17, PFT_18, PFT_19, PFT_20    
Date: 03/22/18   Time: 14:17        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 80        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*    0.09490    0.5378  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on PFT_?        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
PFT_01 -1.22383  0.0029  0.0062  0  0  3.0  4
PFT_02 -0.03171  0.0101  0.0025  0  0  3.0  4
PFT_03 -0.62291  0.0100  0.0123  0  0  1.0  4
PFT_04 -0.00556  0.0099  0.0024  0  0  3.0  4
PFT_05 -0.99072  2931.1  1460.7  0  0  3.0  4
PFT_06 -0.12716  0.0071  0.0025  0  0  3.0  4
PFT_07  0.15569  0.0029  0.0058  0  0  1.0  4
PFT_08 -0.68502  0.0073  0.0082  0  0  0.0  4
PFT_09 -0.11859  0.0047  0.0049  0  0  0.0  4
PFT_10 -0.00416  0.0056  0.0049  0  0  2.0  4
PFT_11 -1.18667  0.0466  0.0316  0  0  3.0  4
PFT_12  0.06418  0.0004  0.0004  0  0  0.0  4
PFT_13 -0.02891  0.0220  0.0298  0  0  1.0  4
PFT_14 -1.03391  152.97  78.899  0  0  3.0  4
PFT_15  0.02476  0.0004  0.0002  0  0  2.0  4
PFT_16 -0.16633  0.0010  0.0004  0  0  3.0  4
PFT_17 -0.89353  0.0033  0.0034  0  0  3.0  4
PFT_18  0.11805  5.E-05  0.0002  0  0  1.0  4
PFT_19 -0.02639  0.0003  0.0003  0  0  0.0  4
PFT_20  0.38588  0.0106  0.0245  0  0  1.0  4
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled  0.00278  0.105  1.249  0.004  1.049    80
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: PFT_01, PFT_02, PFT_03, PFT_04, PFT_05, PFT_06, PFT_07,
        PFT_08, PFT_09, PFT_10, PFT_11, PFT_12, PFT_13, PFT_14, PFT_15,
        PFT_16, PFT_17, PFT_18, PFT_19, PFT_20    
Date: 03/22/18   Time: 14:22        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 60        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -98.5404    0.0000  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on D(PFT_?)        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
D(PFT_01) -1.39650  0.0005  0.0178  0  0  2.0  3
D(PFT_02) -1.54887  0.0090  0.0107  0  0  2.0  3
D(PFT_03) -1.17282  0.0045  0.0308  0  0  0.0  3
D(PFT_04) -1.47111  0.0092  0.0119  0  0  2.0  3
D(PFT_05) -1.49703  5831.3  5163.2  0  0  2.0  3
D(PFT_06) -2.13207  0.0015  0.0114  0  0  2.0  3
D(PFT_07) -0.37808  0.0017  0.0024  0  0  0.0  3
D(PFT_08) -0.71979  0.0064  0.0092  0  0  0.0  3
D(PFT_09) -1.15106  0.0045  0.0048  0  0  2.0  3
D(PFT_10) -1.37289  0.0036  0.0075  0  0  2.0  3
D(PFT_11) -1.57206  0.1042  0.1038  0  0  2.0  3
D(PFT_12) -0.99609  7.E-08  0.0006  0  0  0.0  3
D(PFT_13) -0.60008  0.0049  0.0238  0  0  1.0  3
D(PFT_14) -1.50361  98.720  493.49  0  0  2.0  3
D(PFT_15) -1.42995  0.0004  0.0005  0  0  2.0  3
D(PFT_16) -1.87518  0.0002  0.0015  0  0  2.0  3
D(PFT_17) -1.56731  0.0071  0.0104  0  0  2.0  3
D(PFT_18) -0.27923  3.E-05  5.E-05  0  0  0.0  3
D(PFT_19) -0.90440  0.0004  0.0003  0  0  2.0  3
D(PFT_20) -0.35664  0.0141  0.0159  0  0  0.0  3
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -0.99893 -103.362  1.482  0.004  1.049    60
               
               
Null Hypothesis: Unit root (common unit root process)  
Series: SAL_01, SAL_02, SAL_03, SAL_04, SAL_05, SAL_06, SAL_07,
        SAL_08, SAL_09, SAL_10, SAL_11, SAL_12, SAL_13, SAL_14, SAL_15,
        SAL_16, SAL_17, SAL_18, SAL_19, SAL_20    
Date: 03/22/18   Time: 14:18        
Sample: 2011 2015          
Exogenous variables: None        
Automatic selection of maximum lags        
Automatic lag length selection based on SIC: 0    
Newey-West automatic bandwidth selection and Bartlett kernel  
Total (balanced) observations: 80        
Cross-sections included: 20        
               
               
Method     Statistic   Prob.**  
Levin, Lin & Chu t*   -3.30797    0.0005  
               
               
** Probabilities are computed assuming asympotic normality  
               
Intermediate results on SAL_?        
               
               
  2nd Stage Variance HAC of   Max Band-  
Series Coefficient of Reg Dep. Lag Lag width Obs
SAL_01 -0.13727  0.0294  0.0295  0  0  0.0  4
SAL_02 -0.05156  0.0014  0.0007  0  0  3.0  4
SAL_03 -0.26407  4.E-05  0.0002  0  0  1.0  4
SAL_04 -0.09166  0.0001  0.0032  0  0  1.0  4
SAL_05 -0.37709  0.1235  0.2444  0  0  0.0  4
SAL_06 -0.08393  0.0020  0.0068  0  0  1.0  4
SAL_07  0.00642  0.0096  0.0096  0  0  0.0  4
SAL_08 -0.08884  0.0032  0.0021  0  0  2.0  4
SAL_09 -0.01483  0.0317  0.0111  0  0  2.0  4
SAL_10  0.01105  0.0176  0.0176  0  0  0.0  4
SAL_11 -0.00888  0.0028  0.0006  0  0  3.0  4
SAL_12 -0.01011  0.0053  0.0025  0  0  2.0  4
SAL_13  0.14543  0.1840  0.3833  0  0  1.0  4
SAL_14 -0.07924  0.7585  0.7736  0  0  0.0  4
SAL_15 -0.05884  0.0371  0.0414  0  0  0.0  4
SAL_16 -0.07099  0.0642  0.0705  0  0  0.0  4
SAL_17 -0.00876  0.0070  0.0055  0  0  1.0  4
SAL_18  0.01075  0.0014  0.0014  0  0  0.0  4
SAL_19 -0.00224  0.0003  0.0003  0  0  0.0  4
SAL_20 -0.26096  0.8323  0.2180  0  0  3.0  4
               
  Coefficient t-Stat SE Reg mu* sig*   Obs
Pooled -0.02571 -3.468  1.329  0.004  1.049    80
               
               

Cointegration Test

Kao Residual Cointegration Test  
Series: INV_? LEV_? CF_? LIQ_? PFT_? SAL_?  
Date: 03/22/18   Time: 14:25    
Sample: 2011 2015    
Included observations: 5    
Null Hypothesis: No cointegration  
Trend assumption: No deterministic trend  
Automatic lag length selection based on SIC with a max lag of 0
Newey-West automatic bandwidth selection and Bartlett kernel
         
         
      t-Statistic Prob.
ADF     -12.29880  0.0000
         
         
Residual variance  2.83E+08  
HAC variance    2.78E+08  
         
         
         
         
         
Augmented Dickey-Fuller Test Equation  
Dependent Variable: D(RESID?)    
Method: Panel Least Squares    
Date: 03/22/18   Time: 14:25    
Sample (adjusted): 2012 2015    
Included observations: 4 after adjustments  
Cross-sections included: 20    
Total pool (balanced) observations: 80  
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
RESID?(-1) -1.431406 0.091975 -15.56297 0.0000
         
         
R-squared 0.754028     Mean dependent var 177.5221
Adjusted R-squared 0.754028     S.D. dependent var 18206.07
S.E. of regression 9029.397     Akaike info criterion 21.06678
Sum squared resid 6.44E+09     Schwarz criterion 21.09656
Log likelihood -841.6712     Hannan-Quinn criter. 21.07872
Durbin-Watson stat 0.755428      
         
         

All Sectors’ Firms

Dependent Variable: INV_?    
Method: Pooled Least Squares    
Date: 03/22/18   Time: 13:45    
Sample: 2011 2015    
Included observations: 5    
Cross-sections included: 20    
Total pool (balanced) observations: 100  
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 15406.53 15562.45 0.989981 0.3247
LEV_? 21233.98 26691.94 0.795520 0.4283
CF_? 2.029503 0.252076 8.051143 0.0000
LIQ_? -2383.671 3385.106 -0.704164 0.4831
PFT_? 3013.877 461.0254 6.537335 0.0000
SAL_? -4639.849 8746.287 -0.530494 0.5970
         
         
R-squared 0.571897     Mean dependent var 24115.17
Adjusted R-squared 0.549126     S.D. dependent var 74734.50
S.E. of regression 50182.11     Akaike info criterion 24.54283
Sum squared resid 2.37E+11     Schwarz criterion 24.69914
Log likelihood -1221.141     Hannan-Quinn criter. 24.60609
F-statistic 25.11468     Durbin-Watson stat 1.965192
Prob(F-statistic) 0.000000      
         
         
Dependent Variable: INV_?    
Method: Pooled Least Squares    
Date: 03/22/18   Time: 15:01    
Sample: 2011 2015    
Included observations: 5    
Cross-sections included: 20    
Total pool (balanced) observations: 100  
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 23454.60 13355.62 1.756159 0.0822
LEV_? 2348.678 39264.37 0.059817 0.9524
         
         
R-squared 0.000037     Mean dependent var 24115.17
Adjusted R-squared -0.010167     S.D. dependent var 74734.50
S.E. of regression 75113.46     Akaike info criterion 25.31118
Sum squared resid 5.53E+11     Schwarz criterion 25.36329
Log likelihood -1263.559     Hannan-Quinn criter. 25.33227
F-statistic 0.003578     Durbin-Watson stat 0.072538
Prob(F-statistic) 0.952423      
         
         

Pooled OLS.

Dependent Variable: INV    
Method: Least Squares    
Date: 03/22/18   Time: 11:21    
Sample: 1 100      
Included observations: 100    
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 15406.53 15562.45 0.989981 0.3247
LEV 21233.98 26691.94 0.795520 0.4283
CF 2.029503 0.252076 8.051143 0.0000
LIQ -2383.671 3385.106 -0.704164 0.4831
PFT 3013.877 461.0254 6.537335 0.0000
SALES -4639.849 8746.287 -0.530494 0.5970
         
         
R-squared 0.571897     Mean dependent var 24115.17
Adjusted R-squared 0.549126     S.D. dependent var 74734.50
S.E. of regression 50182.11     Akaike info criterion 24.54283
Sum squared resid 2.37E+11     Schwarz criterion 24.69914
Log likelihood -1221.141     Hannan-Quinn criter. 24.60609
F-statistic 25.11468     Durbin-Watson stat 1.944506
Prob(F-statistic) 0.000000      
         
         
Dependent Variable: INV    
Method: Least Squares    
Date: 03/22/18   Time: 12:39    
Sample: 1 100      
Included observations: 100    
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 23454.60 13355.62 1.756159 0.0822
LEV 2348.678 39264.37 0.059817 0.9524
         
         
R-squared 0.000037     Mean dependent var 24115.17
Adjusted R-squared -0.010167     S.D. dependent var 74734.50
S.E. of regression 75113.46     Akaike info criterion 25.31118
Sum squared resid 5.53E+11     Schwarz criterion 25.36329
Log likelihood -1263.559     Hannan-Quinn criter. 25.33227
F-statistic 0.003578     Durbin-Watson stat 1.920437
Prob(F-statistic) 0.952423      
         
         

Financial Sector Analysis

Descriptive Statistics

  INV LEV CF LIQ PFT SALES
 Mean  2732.483  0.143458  214.1575  2.920280  0.328025  0.201720
 Median  2791.650  0.029683  190.7500  2.958328  0.182800  0.210632
 Maximum  5920.000  0.748311  681.0000  5.955005  1.028239  0.507401
 Minimum  675.6000  0.003687 -71.30000  0.928571 -0.100132  0.021226
 Std. Dev.  1951.525  0.232708  192.3206  1.589384  0.381968  0.168476
 Skewness  0.183850  1.811173  0.605245  0.197900  0.914081  0.374984
 Kurtosis  1.387368  4.953833  2.994904  1.755374  2.217543  1.786060
             
 Jarque-Bera  2.279819  14.11572  1.221095  1.421460  3.295345  1.696752
 Probability  0.319848  0.000861  0.543054  0.491285  0.192497  0.428110
             
 Sum  54649.65  2.869152  4283.150  58.40560  6.560495  4.034409
 Sum Sq. Dev.  72360535  1.028907  702757.2  47.99667  2.772091  0.539302
             
 Observations  20  20  20  20  20  20

Correlation Matrix

  INV LEV CF LIQ PFT SALES
INV  1.000000  0.441840  0.562510 -0.274770 -0.473467 -0.257134
LEV  0.441840  1.000000 -0.155686  0.220655 -0.392868 -0.245157
CF  0.562510 -0.155686  1.000000 -0.050216  0.101638 -0.001191
LIQ -0.274770  0.220655 -0.050216  1.000000  0.534845  0.326675
PFT -0.473467 -0.392868  0.101638  0.534845  1.000000  0.846152
SALES -0.257134 -0.245157 -0.001191  0.326675  0.846152  1.000000

Pooled OLS

Dependent Variable: INV    
Method: Least Squares    
Date: 03/22/18   Time: 12:11    
Sample: 1 20      
Included observations: 20    
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 995.3189 638.2132 1.559540 0.1412
LEV 2987.386 1314.050 2.273419 0.0393
CF 7.178418 1.145940 6.264218 0.0000
LIQ -44.50714 217.0635 -0.205042 0.8405
PFT -4552.526 1638.695 -2.778141 0.0148
SALES 6913.542 2769.343 2.496455 0.0256
         
         
R-squared 0.833704     Mean dependent var 2732.483
Adjusted R-squared 0.774313     S.D. dependent var 1951.525
S.E. of regression 927.1013     Akaike info criterion 16.74533
Sum squared resid 12033235     Schwarz criterion 17.04405
Log likelihood -161.4533     Hannan-Quinn criter. 16.80364
F-statistic 14.03749     Durbin-Watson stat 1.514123
Prob(F-statistic) 0.000049      
         
         
Dependent Variable: INV    
Method: Least Squares    
Date: 03/22/18   Time: 12:24    
Sample: 1 20      
Included observations: 20    
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 2200.924 475.8910 4.624849 0.0002
LEV 3705.334 1773.228 2.089598 0.0511
         
         
R-squared 0.195222     Mean dependent var 2732.483
Adjusted R-squared 0.150512     S.D. dependent var 1951.525
S.E. of regression 1798.675     Akaike info criterion 17.92213
Sum squared resid 58234154     Schwarz criterion 18.02170
Log likelihood -177.2213     Hannan-Quinn criter. 17.94157
F-statistic 4.366422     Durbin-Watson stat 1.746539
Prob(F-statistic) 0.051121      
         
         

Non- Financial Sector

Descriptive Statistics

  INV LEV CF LIQ PFT SALES
 Mean  29460.85  0.315701  4450.491  1.919357  1.760262  1.017985
 Median  2192.500  0.317666  177.0000  1.427767  0.089627  0.941853
 Maximum  415126.0  0.741858  195756.0  9.808824  108.2809  2.257143
 Minimum  112.7000  0.033000 -11706.00  0.612821 -0.739497  0.054388
 Std. Dev.  82786.63  0.165202  22677.54  1.534978  12.37238  0.573509
 Skewness  3.613200  0.314989  7.709386  2.918919  8.208391  0.427771
 Kurtosis  15.13914  2.530971  65.01254  12.49848  70.58317  2.077023
             
 Jarque-Bera  665.2651  2.056204  13610.98  414.3381  16123.32  5.279461
 Probability  0.000000  0.357685  0.000000  0.000000  0.000000  0.071380
             
 Sum  2356868.  25.25610  356039.3  153.5486  140.8209  81.43878
 Sum Sq. Dev.  5.41E+11  2.156046  4.06E+10  186.1364  12092.98  25.98409
             
 Observations  80  80  80  80  80  80

Correlation Matrix

  INV LEV CF LIQ PFT SALES
INV  1.000000 -0.063749  0.605626 -0.031493  0.509638 -0.215308
LEV -0.063749  1.000000 -0.079296 -0.182362 -0.128582 -0.166221
CF  0.605626 -0.079296  1.000000  0.015545  0.116596 -0.203147
LIQ -0.031493 -0.182362  0.015545  1.000000 -0.021826 -0.269428
PFT  0.509638 -0.128582  0.116596 -0.021826  1.000000 -0.074042
SALES -0.215308 -0.166221 -0.203147 -0.269428 -0.074042  1.000000

Pooled OLS

Dependent Variable: INV    
Method: Least Squares    
Date: 03/22/18   Time: 12:08    
Sample: 1 80      
Included observations: 80    
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 31307.39 25583.76 1.223721 0.2249
LEV 6159.236 40680.35 0.151406 0.8801
CF 1.966683 0.287887 6.831442 0.0000
LIQ -2743.506 4420.122 -0.620686 0.5367
PFT 2950.909 520.2430 5.672175 0.0000
SALES -12251.98 12114.44 -1.011354 0.3151
         
         
R-squared 0.570117     Mean dependent var 29460.85
Adjusted R-squared 0.541071     S.D. dependent var 82786.63
S.E. of regression 56083.24     Akaike info criterion 24.77910
Sum squared resid 2.33E+11     Schwarz criterion 24.95775
Log likelihood -985.1640     Hannan-Quinn criter. 24.85073
F-statistic 19.62796     Durbin-Watson stat 1.778690
Prob(F-statistic) 0.000000      
         
         
Dependent Variable: INV    
Method: Least Squares    
Date: 03/22/18   Time: 12:35    
Sample: 1 80      
Included observations: 80    
         
         
Variable Coefficient Std. Error t-Statistic Prob.
         
         
C 39546.35 20149.33 1.962663 0.0533
LEV -31946.34 56625.65 -0.564167 0.5743
         
         
R-squared 0.004064     Mean dependent var 29460.85
Adjusted R-squared -0.008704     S.D. dependent var 82786.63
S.E. of regression 83146.16     Akaike info criterion 25.51927
Sum squared resid 5.39E+11     Schwarz criterion 25.57882
Log likelihood -1018.771     Hannan-Quinn criter. 25.54315
F-statistic 0.318285     Durbin-Watson stat 1.806206
Prob(F-statistic) 0.574259      
         
         

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