i

OBI, CHINEZE EUNICE

PG/Ph.D/06/45559

IMPACT OF EXTERNAL FINANCING ON FIRM PERFORMANCE: EVIDENCE FROM NIGERIA QUOTED

MANUFACTURING FIRMS, 1999 -2012

FACULTY OF BUSINESS ADMINISTRATION

DEPARTMENT OF BANKING AND FINANCE

Paul Okeke

Digitally Signed by: Content manager’s Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

OU = Innovation Centre

ii

IMPACT OF EXTERNAL FINANCING ON FIRM PERFORMANCE: EVIDENCE FROM NIGERIA QUOTED MANUFACTURING FIRMS,

1999-2012

BY

OBI, CHINEZE EUNICE PG/Ph.D/06/45559

DEPARTMENT OF BANKING AND FINANCE FACULTY OF BUSINESS ADMINISTRATION UNIVERSITY OF NIGERIA, ENUGU CAMPUS

ENUGU

SEPTEMBER, 2014

iii

TITLE PAGE

IMPACT OF EXTERNAL FINANCING ON FIRM PERFORMANCE: EVIDENCE FROM NIGERIA QUOTED MANUFACTURING FIRMS,

1999-2012

BY

OBI, CHINEZE EUNICE PG/Ph.D/06/45559

BEING A THESIS PRESENTED IN PARTIAL FULFILMENT OF T HE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY

(Ph.D) IN BANKING AND FINANCE TO THE DEPARTMENT OF BANKING AND FINANCE, FACULTY OF BUSINESS

ADMINISTRATION, UNIVERSITY OF NIGERIA, ENUGU CAMPUS

SUPERVISOR: PROF U.C. UCHE

SEPTEMBER, 2014

iv

DECLARATION

I, Obi, Chineze Eunice, a postgraduate student in the Department of Banking and Finance with

Registration Number PG/Ph.D/06/45559 do hereby declare that this research embodied in this

thesis is my original work. It has not been submitted in part or full to this or other University, for

the award of any Degree or Diploma.

………………………………………………………. Obi, Chineze Eunice PG/Ph.D/06/45559

(Student)

v

APPROVAL PAGE

This Thesis has been approved by the Department of Banking and Finance, Faculty of Business

Administration, University of Nigeria, Enugu Campus, by

…………………………………….. Professor C.U. Uche

(Supervisor)

…………………………………………… Assoc. Professor Chuke .E. Nwude

(Head of Department)

vi

DEDICATION

This thesis is dedicated to God Almighty.

vii

ACKNOWLEDGMENTS

First and foremost, I must say thanks to my supervisor, Professor C.U. Uche, for his support

towards the completion of this study. Sir, I am really grateful.

Special thanks must also go to the former Head of Department, Banking and Finance, Professor

J. U. J. Onwumere, who sat in for my Supervisor and saw the work through, for his interest,

invaluable advice and encouragement throughout the period of this study. I am particularly

grateful for his patience, considerations, suggestions, remarks, material contributions which in no

small way led to the completion of this study. You are instrumental to the success of this work. I

appreciate your efforts. God bless you.

My appreciation goes to all the lecturers and staff of the Faculty of Business Administration and

Department of Banking and Finance in particular, University of Nigeria, Enugu Campus, among

are Prof. Geraldine Ugwuonah, Prof. J. Nnabuko, Prof. U Modum and Prof R. Okafor, Dr. E.K

AgbaezE, Dr. (Mrs.) N. J. Modebe, Assoc. Prof. Chuke Nwude, Dr. Onah, Dr. B. E. Chikeleze, ,

Dr. Okoro Okoro, Dr Obiamaka Egbo and others too numerous to mention.

Also, all staff of Postgraduate School and Bursary Department of the University are highly

appreciated for their contributions in one way or the other. I say thank you for your support and

encouragement.

I also thank my husband, Prof A.W. Obi and my children (Chike, Nnedi, Arinze and Kenny) for

their understanding. My brothers and sisters were also wonderful and I am very grateful for their

support and kindness.

Finally, I thank the Almighty God for his protection and guidance throughout the period of the

study.

Obi, Chineze Eunice PG/Ph.D/06/45559

viii

ABSTRACT The use of external financing can be described as a balancing act between higher returns for shareholders versus higher risk to shareholders. Though external financing can boost stock performance of firms, it is still inconclusive as to its impact on performance of firms in developing economies like Nigeria. It is, therefore, against this background that this study investigated the impact of external financing on earnings per share; pay-out ratio; dividend per share; return on assets and return on equity of Nigerian manufacturing firms. The study adopted the ex-post facto research design. Panel data were collated from the Annual financial Statement of Quoted Manufacturing firms as well as from the Nigerian Stock Exchange Factbook for the period 1999 - 2012. Five (5) hypotheses which state that External Financing does not have positive and significant impact on earnings per share; payout ratio; dividend per share; return on assets and return on equity of Nigerian manufacturing firms were tested using the Ordinary Least Square (OLS) regression technique. The independent variable was External Finance while the dependent variables were earnings per share (EPS), payout ratio (PR), dividend per share (DPS), return on assets (ROA) and return on equity (ROE). The result of this study revealed that External Financing had negative and non-significant impact on earnings per share, payout ratio, dividend per share and return on equity while its impact on return on assets was found to be positive and significant. The implications of the finding reveal that in Nigeria, External Financing does not magnify earnings attributable to shareholders in terms of the book value measures. However, it increases the asset structure of these firms. This study therefore recommends, among others, that Nigerian manufacturing firms should utilize more External Financing in their capital structure up to the optimal level to leverage on the magnifying effect of external financing on shareholder’s wealth.

ix

TABLE OF CONTENTS

Title Page. . . . . . . . . . . i

Declaration. . . . . . . . . . . ii

Approval Page. . . . . . . . . . iii

Dedication. . . . . . . . . . . iv

Acknowledgments. . . . . . . . . . v

Abstract. . . . . . . . . . . vi

Table of Contents. . . . . . . . . .

List of Tables. . . . . . . . . . . x

List of Figures. . . . . . . . . . xi

Chapter One: Introduction . . . . . . . . . 1

1.1 Background of the Study. . . . . . . . 1

1.2 Statement of the Problem. . . . . . . . 7

1.3 Objectives of the Study. . . . . . . . 9

1.4 Research Questions. . . . . . . . . 10

1.7 Scope of the Study. . . . . . . . . 10

1.6 Hypotheses of the Study. . . . . . . . 10

1.7 Significance of the Study. . . . . . . . 11

References. . . . . . . . . . 12

Chapter Two: Review of Related Literature. . . . . . 15

2.1 Conceptual Framework. . . . . . . . 15

2.2 Theoretical Review. . . . . . . . . 18

2.2.1 Financial System and Economic Development. . . . . 18

2.2.2 The Nigerian Stock Exchange. . . . . . . 20

2.2.3 The Financing Decision of the Firm. . . . . . . 21

2.2.4 The Concept of the Firm’s Financing Structure. . . . . 23

2.2.5 Trade–Off Theory. . . . . . . . . 25

2.2.6 The Pecking Order Theory. . . . . . . . 27

2.2.7 The Agency Cost Theory. . . . . . . . 28

2.2.8 Overview of the Modigliani and Miller Theorem. . . . . 31

x

2.3 Empirical Review. . . . . . . . . 37

2.3.1 Capital Structure of Firms. . . . . . . . 37

2.3.2 Capital Structure and Firm Growth. . . . . . . 45

2.3.3 Determinant of Capital Structure. . . . . . . 50

2.3.4 Financing Choices of Firms. . . . . . . . 58

2.3.5 Capital Structure, Small and Medium Scale Enterprises. . . . 60

2.3.6 Capital Structure of Real Estate Firms. . . . . . 61

2.3.7 Capital Structure and Textile Firms. . . . . . . 62

2.3.8 Capital Structure and Firm Ownership. . . . . . 62

2.2.9 External Financing and Access to Finance. . . . . . 63

2.3.10 Determinant of Stock Returns. . . . . . . 70

2.3 Review Summary. . . . . . . . . 74

References. . . . . . . . . . 76

Chapter Three Research Methodology. . . . . . . 88

3.1 Research Design. . . . . . . . . 88

3.2 Sources of Data. . . . . . . . . 89

3.3 Population and Sample Size. . . . . . . . 89

3.4 Explanation of Research Variables. . . . . . . 89

3.4.1 Independent Variable. . . . . . . . . 89

3.4.2 Dependent Variables. . . . . . . . . 89

3.4.3 Control Variable. . . . . . . . . 91

3.5 Model Specification. . . . . . . . . 92

3.6 Techniques of Analysis. . . . . . . . 93

References. . . . . . . . . . 95

Chapter Four Presentation and Analysis of Data. . . . . 97

4.1 Data Presentation and Interpretation . . . . . . 97

4.2 Test of Hypotheses. . . . . . . . . 109

4.3 Implications of Results . . . . . . . 120

References. . . . . . . . . . 123

xi

Chapter Five Summary of Findings, Conclusion and Recommendations 124

5.1 Summary of Findings. . . . . . . . . 124

5.2 Conclusion. . . . . . . . . . 125

5.3 Recommendations. . . . . . . . . 126

5.4 Contributions to Knowledge. . . . . . . . 128

5.5 Recommendation for Further Studies. . . . . . 128

Bibliography. . . . . . . . . . 173

Appendix 1 Ratio Values of Model Proxies. . . . . 129

Appendix 2 Quantum Values of Model Proxies. . . . 147

xii

LIST OF TABLES

Table 4.1 Model Proxies. . . . . . . . 96

Table 4.2 Descriptive Statistics. . . . . . . . 97

Table 4.3 Hausman Test Result of Hypothesis One. . . . . 109

Table 4.4 Regression Result of Hypothesis One. . . . . 110

Table 4.5 Hausman Test Result of Hypothesis Two. . . . . 111

Table 4.6 Regression Result of Hypothesis Two. . . . . 112

Table 4.7 Hausman Test Result of Hypothesis Three. . . . . 113

Table 4.8 Regression Result of Hypothesis Three. . . . . 114

Table 4.9 Hausman Test Result of Hypothesis Four. . . . . 115

Table 4.10 Regression Result of Hypothesis Four. . . . . 116

Table 4.11 Hausman Test Result of Hypothesis Five. . . . . 117

Table 4.12 Regression Result of Hypothesis Five. . . . . 118

xiii

LIST OF FIGURES

Figure 2.1 Trade-Off Theory. . . . . . . . 26

Figure 2.2 MM Proposition 2. . . . . . . . 32

Figure 4.1: External Finance, Earnings per Share Asset structure and Size. . 100

Figure 4.2: External Finance, Pay-out Ratio, Asset structure and Size. . . 102

Figure 4.3: External Finance, Dividend per Share, Asset structure and Size. . 104

Figure 4.4: External Finance, Return on Assets, Asset structure and Size. . 106

Figure 4.5: External Finance, Return on Equity, Asset structure and Size. . 108

1

CHAPTER ONE

INTRODUCTION

1.1 Background to the study

In most developing economies like Nigeria, the financing policies of firms may become relevant

because managers in a company invest in new plants and equipments to generate additional

revenue and income. While the revenue belongs to the owners of the company and can be

distributed as either dividend paid to owners or retained in the firm as retained earnings, the

retained earnings could be used for a new investment or capitalized by using it to issue bonus

shares. But where the retained earnings are not enough to support all profitable investment

opportunities, the company may forgo the investment or raise additional capital, thus altering the

financial structure of firms (Olugbenga, 2012).

According to Pandey (2005) the financial structure of a firm is a long term plan, set up as trade-

off among conflicting interests and identified as the major function of a corporate manager. They

determine the appropriate combination or mix of equity and debt in order to maximize firm

value. This major function of corporate managers has generated so much debate along the

following line; the relationship between leverage and profitability; the optimal mix between

equity and debt and the determinants of corporate financial structure. The underlining

assumption of these debates is to effectively understand the factors that influence the financing

behaviour of firms.

In order to explain and/or understand the financing behaviour of corporate managers, so many

theories have emerged. The earliest is the neoclassical view of finance dominated by the Miller-

Modigliani theorem, also known as the capital structure irrelevance theory (Miller and

Modigliani 1958), according to the theorem, given the assumption that “firms and investors have

the same financial opportunities, under conditions of perfectly competitive financial markets, no

asymmetries of information between different agents and the same tax treatment of different

forms of finance, the corporate financial policy is irrelevant. The theory establishes that, the

stock market valuation of a firm is based exclusively on the earning prospects of the firm and not

on its finance structure. In effect, internal and external finance are viewed as substitutes and

2

firms could use external finance to smoothen investment when internal finance fluctuates

(Yartey, 2006).

Another strand of literature on the financing structure of firms is based on the managerial theory

of investment also known as the modified M-M theorem. Proponents of this theory argue that the

fundamental determinant of investment is the availability of internal finance. Therefore,

managers tend to push investment programmmes to a point that the marginal rate of return is

below the level which would have maximized shareholder’s welfare. The manager pursues

overinvestment policies using internal finance which help them bypass the capital market. This is

usually in the managers’ desires for growth. The bypass of the capital market has the effect of

managers not being subjected to the discipline of the stock market, thus the level of cash flow is

irrelevant for the firm’s investment decisions in neoclassical theory, but rather what matters is

the cost of capital” (Yartey, 2006).

The complexities of today’s business require firms to source funds through internal and external

financing for its operations. External financing options involve financing activities through

public offerings of equity (Ritter, 1991; Loughran and Ritter, 1995; Spiess and Affleck-Graves,

1995), private placement of equity, (Hertzel, et..al 2002), public debt offerings (Spies and

Affleck-Graves,1999) and bank loans, (Billett, et al, 2001). These options that are available for

the financing pattern of firms, though with their disadvantages enable firms to fully tap

opportunities and strengths which maximize shareholder’s wealth as well as ensure future stock

returns.

Another school of thought, generally referred to as the traditional school opine that capital

structure matters and this brought out other financial theories on the issue. These theories

consider various effects of corporate taxation on leverage, capital structure and financial distress;

agency effects, theory of dividend payments, signaling effects and preference of firms for

internal sourcing of funds rather than external (Fabozzi, 2012). The static trade-off hypothesis

views debt to equity ratio as being determined by a trade-off between the cost and benefit of

borrowing. According to Shyran and Myers, (1999), in finding optimum debt ratio, it requires a

trade-off so that the benefit of tax shield is weighed against the backdrop of financial distress.

This will ensure that the firm maintains a healthy debt ratio. Therefore, the static theory of

3

optimal capital structure predicts a point in the activity of the firm at which there is a positive

correlation between debts and return on assets before interest and taxes. At this point, the firms

have more income that will shield them from cost of financial distress.

The pecking-order theory has also tried to explain the financing behavior of firms. According to

Myers and Majluf (1984), the theory states that companies prioritize their sources of financing.

Firstly, firms prefer to use internal financing, secondly, they resort to borrowing when internal

financing is not available and lastly to issuing of equity when both internal and external finances

and debt servicing are not available. The reason for this order according to Beasley et.al (2007)

is the issue of information asymmetries as managers known more about the firm’s performance

and prospects than outsiders. This view holds that managers are likely to issue company shares

when they believe shares are undervalued but will be more inclined to issues when they believe

that shares are overvalued. As such, the assumption is that shareholders are aware of this likely

managerial behavior and thus regard equity issues with suspicion (Beasley et.al, 2007).

The contribution of the pecking order theory in explaining the behaviour of firms can be

observed on why the most profitable firms generally borrow less. According to Berzkalne (2012)

it is not because they have low target debt ratios but because they don’t need outside money.

However, less profitable firms issue debt because they do not have sufficient internal funds for

their capital investment programme and because debt is first in the pecking order for external

finance. The pecking order theory does not deny that taxes and financial distress can be

important factors in the choice of capital structure. However, the theory says that these factors

are less important than managers’ preference for internal over external funds and for debt

financing over new issues of common stock (Berzkalne, 2012).

Conversely, the dynamic model counteracts the static trade–off hypothesis by arguing that

capital structure is not static but changes through time as firms face new developments and new

information about market conditions (Fabozzi et al, 2012). The agency theory states that there is

conflict of interest among shareholders, debt holders and manager, because there arise agency

cost to the firm. These costs in the form of monitoring and restrictive covenants embodied to

4

protect the interests of shareholders and debt-holders against the agency cost, incurred when

managers of firms raise and invest funds so that the wealth of firm is maximized (Pandey, 2005).

In between the dynamic model and agency theory is the dynamic trade – off theory which

stipulates that the business conditions of the firm is not static, but that the firm’s leverage

changes, that is, it is dynamics. In such a situation, firms try to utilize or maximize the conditions

to foster growth opportunities, not holding to the utilization of tax shield. Thus, when these

growth opportunities are envisaged, the agency cost theory comes into play and achieves the

motivation behind the dynamic hypothesis of the trade-off hypothesis.

These theories are based on the findings from developed economies with developed and robust

debt and equity markets. In developing economies such as Nigeria, the debt market is not

developed, and the debt and equity are not alternative sources of funding to a firm. For instance,

equity trading constitutes about 80% of all market activities in the new issue and stock market

(see the Nigerian Stock Exchange Factbook (various years). Also, the government development

stock constitutes more than 95% of total debt traded on the exchange. Such financing constraint

will not give Nigerian firms the latitude to combine equity and debt in line with the above

theories.

The implication therefore, is that firms will rely heavily on external financing in the form of

external or internal equity and less on bank loans depending on their collateral value. This might

also explain the financial mix or structure of Nigerian firms, which is dominated by short-term

debt. Unlike developed economies where the financial structure of firms compose of equity and

debt, the financing structure of firms in most developing economies is mainly equity based and

where debt component is involved, it is usually from deposit money banks or other such financial

institutions (Fodio, 2009). Thus, the payment of dividend becomes relevant to investors as

reflected in stock prices. This could be explained through the dividend signaling hypothesis

(Bhattacharya, 1979; Miller and Rock, 1985). They explained that change in dividend payment is

to be interpreted as a signal to shareholders and investors about the future earning prospects of

the firm. Generally a rise in dividend payment is viewed as a positive signal, conveying positive

5

information about a firm’s future earning prospects resulting in an increase in share price.

Conversely a reduction in dividend payment is viewed as negative signal about future earning

prospects, resulting in a decrease in share price.

Also consistent with bird-in-hand theory argument as developed by Linter (1962) and Gordon

(1963) shareholders are risk-averse and prefer to receive dividend payments rather than future

capital gains. Shareholders consider dividend payments to be more certain than future capital

gains thus a bird in the hand is worth more than two in the bush. Gordon (op cit.) contended that

the payment of current dividends resolves investor uncertainty. Investors have a preference for a

certain level of income now rather than the prospect of a higher, but less certain, income at some

time in the future. The key implication as argued by Linter (1962) and Gordon (op cit.) is that

because of the less risky nature of dividends, shareholders and investors will discount the firm’s

dividend stream at a lower rate of return, thus increasing the value of the firm’s shares.

The effect of external financing on stock returns could also explain the residual effect of

dividend. As argued by the “dividend as a residual” theory, the pay-out ratio of firms is a

function of its financing decision. The investment opportunities should be financed by retained

earnings. Thus internal accrual forms the first line of financing growth and investment. If any

surplus balance is left after meeting the financing needs, such amount may be distributed to the

shareholders in the form of dividends. Thus, dividend policy is in the nature of passive residual.

In case the firm has no investment opportunities during a particular time period, the dividend

pay-out should be one hundred percent. A firm may smooth out the fluctuations in the payment

of dividends over a period of time. The firm can establish dividend payments at a level at which

the cumulative distribution over a period of time corresponds to cumulative residual funds over

the same period. This policy smoothens out the fluctuations of dividend pay-out due to

fluctuations in investment opportunities (Fuei, 2010).

The pricing of securities after the announcement of firms’ external sources of funding tend to be

followed by periods of abnormally low returns, whereas corporate announcements associated

with internal financing tend to be followed by periods of abnormally high returns (Myers and

Majluf, 1984; Myers, 1984). This is especially true in Nigeria where the use of external financing

6

is viewed by investors as sign of inefficiency in the firms’ operations. Finding the right financing

structure encompasses numerous considerations such as growth rate of sales, management risk,

liquidity of assets, etc. Thus, without an appropriate financial structure the growth in sales will

decline, management risk increase, illiquidity of the firms’ assets, loss of control position of the

company which will hinder stock performance of firms.

The use of external financing increases return on equity up to a certain level of operating income

not only in a developing economy like Nigeria but also firms in developed economies As the

firms grow, higher levels of external financing are needed to cover for investment opportunities

available. In a perfect world, management would favor more external financing whenever return

on capital exceeds the cost of internal financing (Kraus and Litzenberger, 1973). However,

higher returns also result in higher risk to the business (risk return tradeoff). Therefore, the use of

external financing is a balancing act between higher returns for shareholders versus higher risk to

shareholders.

Theoretically, it has been established that firms which depend majorly on external financing

must promote their market value through efficient utilization of resources and favourable

dividend policy. For instance, it is argued that in an economy where there is non-availability or

under-development of long-term end of the debt market, firms in such economy will rely only on

the equity market for long-term funding. However, the ability of the firm to raise the needed fund

from this segment of the market will depend on the market perception of the profitability of the

firm, the firm’s reputation and collateral value, the performance of their shares in the secondary

market and past dividend policy.

However, as opine by Yartey (2006), there is no consensus in literature on how such dependence

on external funding could impact on the market value of the firm. For, instance, it is argued that

given the high cost of equity, firms will prefer to finance their activities first with internal fund,

and will resort to equity only when the internal sources are insufficient. If this theory holds true,

the implication is that such firms will declare next to nothing as dividend which could impact

negatively on the pricing of the company’s shares in the secondary market. On the other hand,

7

another school argues that such scenario will put pressure on corporate managers to perform

thereby promoting firm performance.

While the theoretical and empirical standpoints on the above issues have been laid down, few

literature are available to reconcile these theories with realities in developing economies. This

study strived to contribute to literature by examining the impact of external financing on

performance of quoted manufacturing firms in Nigeria.

1.2 Statement of Problem

The Nigerian capital market is skewed towards equity funding which is associated with higher

cost of capital and imposes serious financing constraint on corporate managers. Such skewness

could influence the financing behaviour of corporate managers and the overall performance of

the firm. For instance, the under-development of the long-term end of the debt market could put

so much pressure on corporate managers to perform. Such pressure could enhance performance

or promote short-termism and stymie or hinder long-term investment that promotes performance

on the long-run. The under-development of the debt market could also compel firms to rely so

much on internal funds, thereby restraining their ability to pay dividend.

To empirically ascertain the influence of external funding on stock returns has become

imperative given the level of corporate failure and moribund firms in Nigeria. The Nigerian

capital market which was established in 1960, but started operation in 1961 had 9 government

stock. However, in 1980 following the enterprise promotion decree of 1972, the market

witnessed increased activities as the total number of equity stood at 23 and government

development stock stood at 59. The privatisation exercise which was as a result of Nigerian

government decision to adopt the Structural Adjustment Programme (SAP) in 1986 accelerated

capital market activities within the period. For instance, the value of equity stock which was

N92.4 million in 1973 rose to N348 billion in 1987 and stood at N2, 086.294.59 trillion in 2007.

The value of government development stock also rose from N91.1billion to N307.9mmillion in

1987 and stood at N1.665.4 million in 2006 (CBN, 2012).

Important event that promoted capital market activities in Nigeria was the 2004 banking

consolidation. It will be recalled that in July 6th, 2004, all commercial banks in Nigeria were

8

mandated to shore-up their share capital to N25b by December 31, 2005 or have their licenses

revoked (Donwa and Odia, 2010). Banks in order to comply with this directive used the capital

market option. This singular episode astronomically increased capital market activities. For

instance, the total market capitalization stood at N132.95 billion as at 2007 (CBN. 2012; Ogboru,

2000).

From the above analysis, it is evident that the Nigerian capital market is dominated by equity and

government development stock. The market for corporate bond is not developed and this has

important financing implication for corporate managers in Nigeria. Thus, Nigerian firms will

depend more on equity for permanent source of fund and loans from banks for debt component

of their funding mix. This also explains the absence of long-term debt in financial structure of

Nigerian firms (Ikazoboh, 2011).

According to the trade-off hypothesis, in an environment where a firm is predominantly

externally financed and the market for long-term debt is under-developed, corporate managers

are under pressure to enhance market performance. This is to ensure secure access to the new

issue market according to Baker and Wurgler (2000). Scholars are divided on the influence of

such pressure on firm performance. One school argued that such financing pressure could be the

needed incentive for managers to maximize shareholders’ wealth thus improving firm

performance (Pandey, 2005). Another school, however, argued that such financing pressure

could make corporate managers pursue short term goal (shorter-termism) which could stymie

corporate performance as a result of under-investment in long-term projects (Ujunwa, et al,

2011).

The two conflicting schools are based on the assumption that investors are not myopic and could

effectively monitor managers. This raises an important question on what happens in an economy

that is characterized with investors’ myopia. How do corporate managers’ manipulate market

indicators to promote access to the new issue market? This study strived to clear our

understanding of the financing behaviour of corporate managers in Nigeria, a country that is

characterized by the under-development of long-term debt market and myopic investors.

9

The Nigerian capital market is skewed towards equity funding which is associated with higher

cost of capital and imposes serious financing constraint on corporate managers. Such skewness

could influence the financing behavior of corporate managers and the overall performance of the

firm. The under development of the long-term end of the debt market could put so much

pressure on corporate managers to perform. Such pressure could enhance performance or

promote short-termism and stymie or hinder long term investment that promotes performance on

the long run. The under-development of the debt market could also compel firms to rely so

much on internal funds, thereby restraining their ability to pay dividend. The constraints the

developing economy firms face in sourcing external resources through issuing of equity shares in

their stock market; will bring out the dividend policy decisions of firms.

1.3 Objectives of the Study

The primary objective of this study is to assess the impact of external financing on performance

of Nigerian manufacturing firms. However, this objective was achieved through the following

specific objectives which are:

1. To ascertain the impact of External Financing on Earnings per Share.

2. To determine the impact of External Financing on Payout Ratio.

3. To ascertain the impact of External Financing on Dividend per Share.

4. To ascertain the impact of External Financing on Return on Assets and

5. To determine the impact of External Financing on Return on Equity.

1.4 Research Questions

The study strived to provide answers to the following questions:

1. How far does External Financing have impact on Earnings per Share of Nigerian

manufacturing firms?

2. To what extent does External Financing have positive and significant impact on Payout

Ratio of Nigerian manufacturing firms?

3. To what extent does External Financing have positive and significant impact on Dividend

per Share of Nigerian manufacturing firms?

10

4. To what extent does External Financing have positive and significant impact on Return

on Assets of Nigerian manufacturing firms?

5. To what extent does External Financing have positive and significant impact on Return

on Equity of Nigerian manufacturing firms?

1.5 Research Hypotheses

In line with the research question raised above, the hypotheses for this study were:

1) External Financing does not have positive and significant impact on Earnings per Share

of Nigerian manufacturing firms.

2) External Financing does not have positive and significant impact on Payout Ratio of

Nigerian manufacturing firms.

3) External Financing does not have positive and significant impact on Dividend per Share

of Nigerian manufacturing firms.

4) External Financing does not have positive and significant impact on Return on Assets of

Nigerian manufacturing firms.

5) External Financing does not have positive and significant impact on Return on Equity of

Nigerian manufacturing firms?

1.6 Scope of the Study

This study will cover the period 1999 to 2012. The choice of 1999 is that it heralded the

beginning of uninterrupted democratic rule in Nigeria; therefore, it is assumed that the impact of

democratic rule will open the financial system thereby allowing manufacturing firms to have

access to finance. To accommodate this, the study collected data from 1999 and covered all

selected quoted manufacturing firms on the Nigerian Stock Exchange from 1999-2012 excluding

banked and other financial institutions because of the nature of their funding which is highly

leveraged.

1.7 Significance of the Study

This study is expected to be significant to the following groups. These are:

11

1. Academia

There have been lots of studies on the theory of capital structures and their determinants

worldwide. In Nigeria, studies have also been concentrated on the same issue and have often

failed to explore the relationship between the financing pattern and stock returns. This study is

peculiar because it would deal with the impact of the financing pattern on Nigeria firms.

2. Management of Nigerian Firms

The financing pattern of Nigerian firms was shown to be skewed towards equity holdings. This

study will be significant since financial managers would be able to know the way out of their

dilemma in solving investments policy to pursue.

3. Policy Makers

The study will help researchers to open new line and on related topics while local and foreign

investors will benefit as it will expose the effect of external funding on the values of their shares.

The policy makers both in Nigerian and other countries will benefit from the effective policy

guide on market department, breadth and sophistication of the capital market, towards enhancing

the debt/fixed income capital market and transparency and accountability in the capital market,

and above all implement clear structures for policy co-ordination across financial service

industry regulators. It will also help in checking the decline in share prices by establishing a

capital market stabilization fund and the liquidity situation in the economy.

12

REFERENCES

Baker, M & Wurgler, J. (2000). The equity share in new issues and aggregate stock returns. Journal of Finance. 55(12):2219-2257.

Beasley R.A., Myers S.C & Marcus, A.J (2007). Fundamentals of corporate finance. 5th ed. Boston: McGraw-Hill/Irwin. Berzkalne, I (2012). Theories of optimal capital structure: assessment and application. New Challenges of Economic and Business Development. Riga, University of Latvia. 145-164. Bhattacharya, S. (1979). Imperfect information, dividend policy, and “the bird in the hand fallacy. Bell Journal of Economics. 10(5):259-270. Billett, D., J Flamery & Garfinkel, H. (2001). Internal funds, moral hazard, post-financing stock underperformance. Journal of Financial Economics. 37(9):2651-2669. CBN (2012). Statistical bulletin. Central Bank of Nigeria, Abuja, CBN Donwa, P & Odia J. (2010). An empirical analysis of the impact of the Nigerian capital market on her socio economic development. Journal of Social Sciences. 24(2):135-142. Fabozzi G. et al, (2012), Financial Economics, U.S : John Wiley and Son Inc. Fodio, M. I (2009). The dividend policy of firms quoted on the Nigerian stock exchange: an empirical analysis. African Journal of Business Management. 3(10):555-566. Fuei, L.K (2010). The Information Content of Dividend Policy on Future Earnings in Australia: A VECM Approach. International Research Journal of Finance and Economics. 49(1):68-86. Gordon, M. J. (1963). Dividends, earnings and stock prices. Review of Economics and Statistics. 41(3):99-105. Hertzel, G., D. Lemmon, M. Linck & Rees, T (2002). An empirical review of stock returns in OCED countries. Journal of Economic Review 25(22)2341-2362. Ikazoboh L (2011). An assessment of the Nigerian capital market. Nigerian Capital Market Bulletin. 12(5):67-82. Kraus A.,& Litzenberger, R.A (1973). A state preference model of optimal financial leverage. Journal of Finance, 28 (7):911-922. Linter, J., (1962). Distribution of incomes of corporations among dividends, retained earnings, and taxes. American Economic Review. 46(5):97-113.

13

Loughran R. & Ritter, R. (1995). The stock market and the financing of corporate growth in Eastern Europe: the case of Ukraine. International Monetary Fund Research Department WP/09/223 IMF Working Paper Miller M & Modgliani, F. (1958). A Review of the capital structure theories. The Journal of Economies, University of Oradea. 3(1):315-320.

Miller, M. & Rock, K. (1985). Dividend policy under asymmetric information. Journal of Finance. 40(6):1031-1051. Myers S.C. & Majluf, S.N (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics. 13(4):187-221. Myers, S. (1984). The capital structure puzzle. Journal of Finance. 39(8):575–97. Ogboru I. (2000). The Nigerian capital market up to 1997: an assessment. Journal of Economics and Management studies 1(2):3-19.

Olugbenga, A.A (2012). Information content of dividend: evidence from Nigeria. Developing Country Studies. 2(2):74-83 Pandey, I M (2005). Financial management. Nineth Edition, New Delhi Oikes Publishing House PVT Ltd

Ritter, O.R. (1991). Long–run performance of initial public offerings. Journal of Finance. 46(1):3-37.

SEC (2012). Nigeria’s capital market, making world class potential a reality: “The report of the SEC Committee on the Nigerian Capital Market

Shyran M. & Myers, S. (1999). Capital structure and profitability: case of Islamabad stock exchange. International Review of Business Research Papers. 3(5):347-361 Spies J. & Affleck-Graves, B (1999). External financing for development and international financial instability. Research papers for the Intergovernmental Group of Twenty-Four on International Monetary Affairs by No. 32 Spiess J. & Affleck-Graves, B. (1995). Testing the pecking order theory of capital structure. Journal of Financial Economics. 67(3):345-367 Ujunwa, A. et al (2011). The global financial crisis: realities and implications for the Nigerian capital market. American Journal of Social and Management Sciences. 11(3):2151-1559.

Yartey, C.A. (2006). The stock market and the financing of corporate growth in Africa: the case of Ghana. Journal of Financial Intermediation. 56(12):1205-1234.

14

CHAPTER TWO

REVIEW OF RELATED LITERATURE

2.1. Conceptual Framework

The main sources of external financing available to firms are equity and debts. The equities are

owners of the firms and the profit divisible to them are the ruminants’ after other interest holders

have been serviced. Debt is that which is owed, either to creditor or money borrowed, but the

term can also cover moral obligations and other interactions not requiring money. According to

Swanson and Marshall (2008), debt is a means of using future purchasing power in the present

before a summation is earned. A firm uses various kind of debt to finance its operations. The

various types of debt can generally be categorized into; (I) Secured and unsecured debt; (2)

private and public debt and (3) syndicated and bilateral debt and other types of debt that display

one or more of the characteristics noted above (Swansan and Marshall, 2008). Pandy (2005)

talks about debt capacity of a firm and defines it as the amount which a firm can easily and under

adverse condition employ. This means that if the firm is unable to service, it can lead to

financial distress. In this Fabozzi et al (2012) states the above in terms of the free cash flow, and

state that Jensen (1986) had argued that by using debt financing, a firm reduces its free cash

flows. The theory of Jensen’s free cash flow theory stipulates that the need to issue debt benefits

the firm in two ways; (i) fewer resources are under the control of management and there are less

chances of wasting the resources in unprofitable investments, (2) by continually depending on

the debt market (capital market) to raise new capital imposes a governance die and plan on

management, that would not have been so. (Fabozi et al, 2012)

Brigham, (1995) states that debt allows people and organizations to do things that they would not

be able or allowed to do commonly, for instance, people in industrialized nations use it to

purchase houses, cars and many other things too expensive to buy with cash. However,

companies use debt in many ways to leverage the investments made in their assets that is

leveraging the return on equity. Therefore, to firms, leveraging the proportion of debt to equity

is considered most important in determining the riskiness of an investment, the more debt per

equity, the more risky. As agreed by Grunewald and Erwin (1970) a public corporation may

leverage its equity by borrowing money, they say, the more the firm borrows, the less equity

15

capital it needs, so that profit or loss are shared among smaller base and are proportionately

larger as a result.

Traditionally, the tax savings that accrue to the firm as a result of the firm’s use of debt finance

come in the form of interest which is deducted while equity is not, and has been the major

benefit of debt (Kraus and Citzenberger, 1973). Other benefit of debt include committing

managers to operate efficiently (Jeuseu 1984), and engaging lenders to monitor the firms (Jenseu

and Menkling 1976) thus, while the cost of debt includes the cost of financial distress (Scott,

1976) personal taxes, (Milles 1977), debt overhang, (Myers, 1977), and agency conflict between

manager and investors or among different groups of investors (Binsbergeu, et al. 2007).

However, empirical results in this area are much and are somewhat mixed and as a result, a

number of empirical regularities have been documented. Large firms with tangible and few

growth options tend to use a relatively large amount of debt (Rajau and Zingales, 2003). Frank

and Goyal (2004) are of the view that firms which high corporate tax rates tend to have higher

debt ratios and use more debt incrementally (Grahan et al, 1998).

Examining the benefit of debt from an empirical product of Odecol, Tim at al. (1997) state that

there are varieties of potential benefits from debt financing, hence, to them, a heavy use of debt

is likely to produce efficiency in companies with plenty free cash flows that do not require much

additional capital to fund investment requirement. In such circumstance, substituting for equity

is likely to add value by strengthening management incentive to increase future cash flows and

return excess capital to investors. This was confirmed by Jensen (1986), Gross man and Hart

(1982) and Stulz (1990). In fact, Jensen, (1986), argue that managers often prefer to grow the

firm beyond its optimal size. This may be the case, because according to him, they have the

compensation contract based on the measure of firm size or their desire to lift up the firm from

small to large business. If this is the case then, manager may invest in projects that increase firm

size, but have a negative impact on shareholders’ value. However, as opined by Servaes and

Tufano, (2006), a firm with huge debt financing would be prevented to some extent from

engaging in this managerial self servicing behavior because, the cash-flows generated by the

assets of the firm cannot all be reinvested; instead they need to be employed to service debts in

the form of interest. This is valuable if their alternative use was in projects that destroy value for

16

shareholders. To them, debt thus serves as a bonding device on the part of managers where they

commit themselves not to over invest (Servaes and Tufano, 2006). The managerial self interest

argument thus implies a positive relationship between firm value and the amount of debt

outstanding. Debt increases the value of the assets of the firm because it prevents managers from

wasting resources according to Denis and Denis (1998) but of course, this is only part of the

argument. Kaplan, (1998) finds out that, not only will debt prevent managers from misallocating

resources; it also forces manager to run the current operations mere efficiently so that there are

funds available to service the debt and also make managers to carefully examine the component

of current assets in the financial structure, whether they would be worth more if sold off.

In the area of interest payment being tax deductible while dividend payments to equity are not,

taxes also provide an important and quantifiable benefit of debt financing. According to Tim et

al (1997), it gives a clear reason why firms can borrow, rather than issue equity. Inseloberg and

Kaufold (1997) state that the value of the tax shield provided by debt in a given year is a function

of the interest paid and the marginal tax rate.

Tim et al (1997) state that a firm that expect low earning in the future will not be able to benefit

greatly from the tax shield afforded by debt and should have relatively low amount of debt in

their balance sheet (all things being equal). However, they continue, that a firm with high

expected future earnings could take more debt as a means of shielding earnings from taxes. The

important point here is that the expected benefit of debt financing is greatest when corporate

taxable earnings and free cash flows are projected to be both large and predictable. The cost of

debts is highest when earnings and cash flows are low and uncertain.

Debts are costly when a firm cannot cover its interest expenses because of an earnings short fall,

a condition called financial distress (Robichet and Myers, 1965). This penalty can be justified by

a variety of arguments relating to the reaction of firms stakeholders to its average. Customers,

suppliers, employers, competitors and government are concerned about firms’, financial

condition because they understand that a financial distress firm behaves differently from a

healthy firm. Sheridan (1984), Alan and Sheridan (1985), opined that a financially distressed

firm is much more likely to go into liquidation a financially healthy firm. When companies go

out of business or just threaten to do so, there are often spill over cost imposed on nonfinancial

17

stakeholders, thus more sophisticated workers, suppliers and customers anticipate the cost

associated with doing business with a firm that may be liquidated. To Bolton and Schartstein

(1990) a firm’s financial distress may invite a predatory response from competitors , thus making

a bad situation worse and apart from this the debt under investment theory states that financially

distressed firms are more likely to choose to forgo value increasing investment in market share

(Bolton and Schartstein, 1990).

To Myers (1997), as firm adds more debt it will not only cut projects that do not add value but

may also have to start cutting projects that do add value because funds will not be available to

service the debt. This is the debt overcrowding argument of the cost of debts, thus while debt

may prevent firms from making investment, it also prevents making good investments (Myers

1977). Therefore, an optimal amount of debt must be chosen to minimize joint costs of under

investment. Stulz, (1990) gives a model formalizing this trade off. The empirical evidence for

the fact that debt may sometimes prevent firms from investing optimally is also found in

Shivdasami, (1993). Servaes and Tufana (2006) state that debt may sometimes prevent firms

from making good investment, is also another theoretical motivation for the flexibility argument.

2.2 Theoretical Framework

This section reviews the related theoretical literature on previous scholarly works on finance and

stock performance. The Nigerian capital market has been indentified to be skewed towards

equity holdings, followed by few government development stocks and bank loans. In this

circumstance, the firm managers face tremendous challenges in sourcing external financing,

since their market value must be enhanced through profit maximization and liberal dividend

policy. This dilemma of how to generate funds in the context of financing pressure was ex-rayed

in this study.

2.2.1 Financial System and Economic Development

The relationship between the financial system and economic development has been in the

literature argument for a long term. First, there are many definitions of financial system. Okafor

(1983) opined that financial system consists of financial intermediaries, financial markets,

18

financial rules, conventions and norms that help in the flow of funds in the economy. Also, that

the system is controlled by government through its nominated agency. Fabozzi et. al (2012)

state that financial system could be defined through its functions, which are tasks performed by

the system. These tasks are performed through alignment in the financial deals involving either

commitment of financial resources, that is, fund raising or reallocation of risks. Therefore, they

define financial system through its functions of clearing and settling payments, pooling

resources, transferring resources, managing risks, producing information and managing

incentives. Dudley and Hubbard, (2004) identify that capital markets enhance economic

performance, through facilitating allocation of capital and risk and thereby providing job

creations, raising productivity growth rate with lower unemployment rate and also that an

effective capital markets will help the country in enforcing laws and property rights transparency

and accuracy in accounting and financial reporting.

In the literature, there has been arguments about the advantages and disadvantages of bank based

financial systems and market based systems. Levine (1999) and Demirgue-Kunt and Maksimoive

(1999) state that financial structure whether bank based or market based exert influence on

economic growth and firm performance. Also Demirgue–Kunt and Levine (2000) examine the

relationship between financial structure and economic development and the link between

financial system and legal, regulatory and policy determinants.

Their results show the followings:

• Banks, non banks and stock markets are larger, more active and more efficient in richer

countries, also financial systems, on average, are more developed in richer countries.

• In higher income countries, stock markets become more active and efficient relative to

banks. There is some tendency for international financial systems to become more

market oriented as they become richer.

• Countries with a common law tradition, strong protection of shareholder rights, good

accounting regulations, low levels of corruption and no explicit insurance tend to be more

market based

• Countries with a French Civil law trading, poor protection of shareholder and creditor

rights, poor contract enforcement, high levels of corruption, poor accounting standards,

19

restrictive banking regulations and high inflation tend to have underdeveloped financial

systems.

Kunt et al (2013) categorized the components of that where a country has the followings, poor

contract enforcement, high levels of corruption and poor accounting standards, the financial

systems tend to be under developed. The Nigerian financial system has been classified under an

emerging economy which is still under developing with high levels of corruption and poor

accounting standard.

Ozurumba and Chigbu (2013) in their paper on economic analysis of capital market performance

on economic growth of Nigeria stated that there is a significant impact of capital market on

economic development and that capital market affects economic growth through all share index,

value of shares traded and number of deals. Siglitz (2000) had identified that there is need for

intervention in short term capital flows in the argument of capital market liberalization and

instability. The case of developing countries is also x-rayed as having low regulatory capacity in

the financial sector and with less diversified economies and weaker role of automatic stabilizers.

He recommends that developing countries should have strong financial institutions and

regulatory structure to be in place before liberalizing their capital accounts.

2.2.2 The Nigerian Stock Exchange:

The Nigerian stock Exchange, (NSE) was founded as the Lagos stock Exchange, in 1977. The

name was changed to Nigerian stock Exchange (NSE). Nigerian stock Exchange has its

headquarters in Lagos, with branches in Kaduna, Port Harcourt, Kano Onitsha, Ibadan, Abuja

and Yola. The Nigerian stock Exchange has about 283 securities including 11 government

stocks 49 industrial loan stocks comprising of debentures or preference stocks and 194

equity/ordinary shares of companies. Nigerian stock exchange is a self regulatory organization

and the transactions are regulated by the Securities and Exchange Commission (SEC). The NSE

is helped in running by various houses that deal in stock brokerage, issuing firms and practicing

corporate law houses, auditing and accounting houses. Previously, there is a system of call over

for trading but gone are these days, presently an Automated Trading System (ATS) has been in

place. The clearing, settlement and delivery of transactions are electronically in place now and

20

in charge is the Central Securities Clearing System Limited (CSCS). This makes the processing

faster and effective. In 1984, the All-share Index (AMI) of the Nigerian stock Exchange began

to use only common stock, that is ordinary shares for its calculations (Donwa and Odia, 2010).

The Nigerian stock Exchange has helped in enhancing corporate finance in Nigeria, in the area of

raising capital from firms in its operations in order to develop the economy of the nation. Sule

and Momah (2005) agree that stock market contributes to the economic growth of emerging

economies and helps to explain the variables in the economic growths of developed nations. The

earlier work of Goldsmith (1969) states that stock Exchange plays an increasing role not only in

channeling resources but helps in promoting reforms that are used in modernizing the financial

sector. Levine (1991) also states the two key ingredients on how stock exchange speeds up

economic growth, first by making the exchanges of shares of quoted companies possible without

much interference in the productive processes and secondly by offering portfolio diversifications

to the investors. By inference the Nigerian stock exchange is expected to achieve these goals.

2.2.3 The Financing Decision of the Firm

Pandey (2005) state the four major decisions managers of firms make as follows: Investment

decisions, financing decisions dividend decision, and liquidity decision. Then he postulated that

the financial manager strives to maximize the market value of its shares by ensuring that

decisions made on the above functions help to enhance the value of the firm. In these functions

of managers of firms, Chance (2005) and Damodaran, (2002), state that to achieve these goals,

firms often require that any corporate investment be financed adequately because the financing

mix of the firm can impact of the firm valuation. Okafor (1983) state also that the sources of

financing used by the firm comprise some combination of debt and equity. Hence according to

Pandy (2005) the management must match the financing mix to the assets being financed as

closely as possible in terms of both timing and cash flows in order to achieve the overall

objective of the firm which is shareholders wealth maximization.

Myers (2002) opined that there are four major theories that evaluate the firm’s financial

decisions. They are (1) Modigliani and Miller (1958) theory of financial structure irrelevance.

Here, the firm’s value and real investment decisions are unaffected by the financing decisions of

the firm (Modigliani and Miller, 1958), (2) the Trade off theory in which firms balance the tax

21

advantage of borrowing against the cost of financial distress. In this case, firms are assumed to

trade off the tax benefits of debt with the bankruptcy cost of debt when making decision (see

Kraus and Lichtenberger, 1973), (3) the Agency cost theory which shows in financing, how

managers respond to their personnel incentives, (Jensen and Meckling, 1976) and (4) the Peeking

– Order theory, where financing is used to migrate the problem of the existence of the

asymmetries information between insider and outsider investor. Here, it is suggested that firms

avoid external financing while they have internal financing available and avoid new equity

financing while they can engage in new debt financing at reasonably low interest rate (Myers and

Majiluf, 1984).

However, there is another new emerging theory, the market timing hypothesis which states that

the firms look for the cheaper type of financing regardless of their current levels of internal

sources debt and equity (Baker and Wurgler, 2002). These theories of financing are conditional

as explained by Myers (2002) is not general, thus he explains that it is easy to find examples of

each theory at work but otherwise difficult to distinguish the theories empirically. Concluding,

he says that large safe firms with tangible assets tend to borrow more in their financing decision

while firms with high profitability and variable growth opportunities tend to borrow less. Each

of these tendencies is consistent with two or of the major theories of financing. Therefore, it may

be possible to devise sharper tests by exporting the theories to developing economies where

agency and information problems are more severe. This view was supported by Morgaritis and

Psilliki (2008) who said that corporate financing decisions of the firm have quite complex

processes and existing theories cannot best explain only certain facets of the diversity and

complexity of financing choices. However, because of the complexities of these financing

decisions, Zingales (2000) held that we need new foundations for the firms’ financing decisions

and as Myers (2002) put it, the foundation will require a deeper understanding of the motives and

behaviours of managers and employees of the firms in achieving the overall objectives of

shareholders wealth maximization.

22

2.2.4 The Concept of the Firms’ financing Structure

Since the seminal work of Modigliani and Miller in 1958, the concept of the firms’ financing

structure has been a major discussion in finance literatures. Several scholarly work have been

done in this area (MM 1958; MM 1963; Myers and Majuf, 1984; Baker and Wurgler, 2002;

Lyandres and Zhdaner, 2007; Rosenbauin and Pearl, 2009; Gajurel, 2004; Simerly and

Mingfang, 2000; Champien, 1999; Kraus and Litzenberger, 1973; Miller, 1979; Myers,1984; De

Angelo and Masulis 1980; Kin 1979; Jensen and Meekidng 1976; Grossman and Hart, 1982;

Sultz, 1980; Harris and Ravio, 1990; Ross, 1977; Allen and Wharten 2002; Haris and Raviv,

1991; Myers, 2001; Jensen 1986; William, 1986; Myers, 2002; Gram 2000; Boodhoo 2009,

among others) These works have tried to extensively review the impact of the financing structure

on the value of firm from several perspectives.

Boodhoo, (2009) describes financing structure as a mix of debt and equity capitals maintained by

the firm and also conclude in line with other definitions that financing structure is very important

since it relates the ability of the firm to meet the need of the shareholders. Therefore, an

appropriate financing structure is critical decisions for any business organization. The

importance of the financing structure decisions cannot be more accurately summarized than the

conclusion of Simerly and Mingfang, (2000) when they said that financial structure decision is

important not only because of the need to maximize returns to various organizational

constitutions but also of the impact such decisions has on the organizations ability to deal with its

competitive environment. The prevailing arguments were originally developed by MM in 1963,

which assumes that an optimal financial structure exists for a firm that balances the risk of

bankruptcy with the tax savings of debt and once, such is established; this financial structure

could provide greater returns to shareholders than they would originally receive from all equity

firm. The above was affirmed by Hatfield et al (1994) and Brighaim and Gapenski (1996).

Infact, Brigham and Gapenski op cit agree that in theory, the MM is valid. However, in practice,

bankruptcy costs do exist and are directly proportional to the debt level of the firm.

Looking back at the conclusion of MM, the question which have always been in the minds of

several researchers are: Is there any optimal financial structure for firm that would maximize the

wealth of shareholders? If there is, how then do we achieve such optimal structure? Sultz,

23

(1990) supports the idea of optimal financial structure of financing that would result from a

compromise between benefits related to the reduction of cash flows and the inconvenient that

these cash flows will bring when there are good investments opportunities. As for Chen and Kin

(1979) they argued that it is suitable to look for optimal financial structure through arbitrage

between tax benefits on one hand and substitute of debts and risks of bankruptcy on the other

hand. Concerning the issue of the choice of optimal financial structure, Jensen and Meckling

(1976) argued that in the presence of taxes on profits, firms have interest to issue debt assets

because this will generate substantial tax economies which may boost the value of the firm

proportionately to increase the debt ratio.

Nevertheless, it must be said here that issuing debt may lead to increasing agency cost. In

deriving an optimal financial structure for the firm, Champion, (1999) was even of the view that

the use of debt was one way to improve the performance of an organization. While this can be

true in some circumstances, it fails to consider either the complexities of the competitive

environment or the long term survival needs of the organization (Simerly and Mingfang, 2000).

Thus, Simerly and Mingfang op cit were of the opinion that when firms use debts to either

discipline managers or to achieve economic gain, it is the easy way out, for some in instances; it

can lead to the demise of the organization, thus, contributing to the fact that there may not be an

optimal financial structure. Continuing, they believe that the original question was not framed

correctly. Therefore, rather, than ask what an optimal mix of debt and equity that will maximize

shareholder wealth will be, the question should be under what circumstances should debts be

used to maximize shareholders wealth and why? Thus, they found that many firms do not have

an optimal capital structure and the reason advocated by these firms was that in general, the

performance of a firm is not related to the compensation of the managers of the firm.

Also in reviewing earlier works of MM, Miller (1977) argued that the tax advantage of debt is

exaggerated by considering the corporate profit in isolation from personal tax. He argued that

the corporate tax advantage of debt is offset if personal tax rates on investors’ debt income are

higher than tax rates on equity income. In addition, Berman and Schwartce, (1978) argued that

the corporate tax advantage of debt is lower because the interest tax shield is lost if firm goes

through liquidation and bankruptcy. Furthermore, De Angelo and Maslilds (1980) argued that

24

the substitute of tax shields such as investment tax credits also reduce the corporate tax

advantage. Thus, no optimal financial structure exists for the firm.

Consistent with the various questions raised concerning the optimum of the firm’s financial

structure, another question that has been asked is can the MM irrelevancy theory of financial

structure hold in real world? In answer to this question, MM had done this on the basis of certain

assumptions which in practice may not work. In fact, they assume a perfect capital market ie, no

transaction or bankruptcy cost, perfect information, thus firms and individuals can borrow at the

same interest rates, no taxes and investment decisions are not affected by financing decision

Brealey et al (2004). Thus, if financial structure is irrelevant in a perfect market, then, the

imperfection which exists in the real world must be the causes of its relevance. Several theories

have been advocated by several scholars, such theories like trade off theory (static and dynamic)

of financial structure, pecking Order theory, Agency cost theory, market timing hypothesis

(Baker and Wurgler, 2002), Accelerated investment effect theory (Lgaudress and Zhdanor, 2007)

and dividend payment theory among others. However in this research, focus is made on these

theories and how they can be explained in relation to the financial structure of the firm.

2.2.5 Trade-Off Theory

The classical version of the trade off theory of financial structure goes back to Kraus and

Litzenberger’s 1973 seminal work. They consider the balance between the dead weight cost of

bankruptcy and the tax savings benefits of debts. According to them, the trade off theory is an

idea that a company can choose how much debt finance and how much equity finance to use by

balancing the cost and benefits of debt in the financial structure of the firm. The theory

propounded to counter the perfect market assumptions of Miller and Modigliani (1958) and

suggest that in real world, bankruptcy costs exist in firms. Kraus and Litzenberger op cit

conclude that there is an advantage of financing with debt, as well as cost incurred. Therefore,

there is a marginal benefit of further increases in debt decisions. As debt increases, the marginal

cost increases, so that a firm that is optimizing its overall value will focus on this trade off when

choosing how much debt and equity to use for financing. The importance of the theory is that, it

explains the fact that corporations usually are financed partly with debt and partly with equity.

The diagram below typically captures the trade off theory.

25

Figure 2.1 Trade-Off Theory

Trade –Off PV(Bankruptcy cost)

5 PV (Interest tax

Shield

2

Firm

value

0 Debt/Equity

Source: Simerly and Mingfang, (2000).

As seen from the above as the debt, equity value (i.e., leverage) increases, there is a trade-off

between the interest tax shield and bankruptcy cost causing an optimal capital structure.

The empirical relevance of the trade off theory has often been questioned. Miller, 1977 states

that taxes are large and sure while bankruptcy is rare, therefore it has a low dead weight cost.

Accordingly, if the trade off theory were true, then firms ought to have much higher debt level

than we often observe in reality. Myers, (1984) was even a more particular fierce critic of the

theory. In his presidential address to the American Finance Association meeting, he proposed

the pecking order instead of the trade off theory. Shyem et al (1999) opined that optimum

normally require a tradeoff between the tax advantages of borrowed money and cost of financial

distress when the firm finds it has borrowed too much. Also, Myers (2003) stated that in static

trade off theory optimal capital structure is reached when the tax advantages to borrowing is

balanced at the margin, by costs of financial distress. Fama and French, (2002) also criticized

not only the trade off theory but the pecking order theory as well in a different way.

However, Graham, (2000) in his contribution to the trade off theory used it to examine the

interest tax spread between corporate bonds and tax exempt municipal bonds to estimate the tax

rate paid by marginal investors in corporate bond empirically and found that the theory may

explain differences in D/E ratio (Debt/Equity) between industries but it does not explain

differences within the same industry Dynamic Model (Dynamic Trade-off Hypothesis). The

dynamic model relates to the ways capital structure changes from time to time as firms adjust

26

both to new developments in their own positions and new information available about market

conditions (Fabozzi et al, 2012). By this definition the model states that an optimum financial

structure is not static, but rather changes from the factors stated in the definition. Thus, for a

target capital structure with the above stated (new developments and market conditions), the firm

adjusts to take benefits in taking decisions that deviate from time to time to the chosen target

structure.

There are other model options developed by other schools, for instance, Morellec and Schuchiff

(2007) developed a real options model where differential tax treatment of capital gains and

income can adversely affect a firm’s policy choices. Morellec and Schverhoff (2009) stated

that because of the effects of the asymmetric information on firms’ investments and financing

decisions as the firms raise external funds; the corporate insiders can signal private information

to outside investors by altering either the dividend from investment or the firm’s debt equity or

both. This may result in equity issues, because of information asymmetries being more attractive

than debt. Therefore, the firms try to keep the growth opportunities open, in order to maximize

shareholders’ value and not holding on utilizing the tax shields. Then, the essence of this

dynamic hypothesis is that it is a trade off hypothesis between various options available for firm

managers.

2.2.6 The Pecking-Order Theory

The Pecking Order Theory or Pecking Order Model was developed by Stewart Myers and

Nicolas Majluf in 1984. It states that firms prioritize their sources of financing according to the

principle of least effort or of least resistance, preferring to raise equity as a financing means of

last resort (Simerly and Mingfang, 2000). Hence, the internal funds are used first and when it is

depleted, debt is raised and when it is not sensible to issue any more debt, equity is issued. As

postulated by Myers and Majluf (1984) the theory tries to capture the cost of asymmetric

information, thus the form of financing mix a firm chooses can act as a signal of its needs for

external finance. In fact, they argued that equity is a less preferred means to raising capital

because when managers (who are assumed to know better about the true condition of the firm

than investors) issue new equity, investors believe the firm is overvalued and managers are

27

taking advantage of this overvaluation, as a result investors will place a lower value to the new

equity issuance.

This confirmed the opinion of Simerly and Mingfang (2002). In supporting the above opinion,

Myers (2002), agreed that investors do not know the true value of either the existing assets or the

new opportunity, so they cannot exactly value the shares issued to finance the new investment.

Various test carried out as regards the pecking order theory have not been able to show that it is

of first-order importance in determining a firm’s capital structure as postulated by Myers and

Majluf (1984). However, several authors have found that there are instances where it is a good

approximation of reality. As confirmed by Fama and French (2002) and Myers and Shyam-

Sunders (1999) who found that some features of the data were better explained by the pecking-

order than by trade-off theory. However, Goyal and Frank (2003) showed among other things

that pecking order fails where it should hold, namely, for small firms where information

asymmetry was presumably an important problem (Goyal and Frank, 2003).

2.2.7 The Agency-Cost Theory

One of the defining characteristics of business in the 1990s was the adoption of the Agency

theory to address the managerial excesses of the 1970s and 1980s (Simerly and Mingfaing,

2000). The classical Agency concept was developed by Berle and Means (1932). They observed

that ownership and control which have been separated in larger corporations as a result of

dilution in equity positions provided an opportunity for professional managers to act in their own

best interest. Thus, the Agency theory attempted to provide explanation to firm behaviours in

the area of choice financing. The earlier works of Berle and Means (1932), Jensen and Meckling

(1976) and Grossman and Hart (1982) were seen as pioneer in Agency theory research, their

analyses permitted the building up of interlink between the organization and the agency theory of

corporate finance.

Since the seminal paper of Jensen and Meckling in 1976, vast literatures on the agency theory

explanations of financial structure have been developed (Harris and Raviv, 1991; Myers 2001).

As stated by Simerly and Mingfang, (2000), much of the activities of management are associated

28

with increasing the size of the organizations and management were motivated not by a desire for

maximizing shareholders wealth but by opportunities for the self aggrandizement, therefore,

contractual device suggested by Agency theory to accomplish the transfers of wealth from the

organization to the investor is debt creation for the shareholders. Thus, debt provides a means of

bonding managers promises to pay out future cash flows and as well as providing the means for

controlling opportunistic behaviour by reducing the cash flows available for discretionary

spending thus ensuring that top managers attention is then clearly focused on those activities

necessary to ensure that debt payments are made. As supported by Ross (1977), a performing

firm is one that borrows and is capable of honouring its commitment for reimbursement without

any serious problem. By contrast, a bad firm is one that acts similarly but is a posterior, inapt to

face debt reimbursement.

Agency theory also has important implications for the relationship between equity holders and

debt holders (Simerly and Mingfang, 2000). Thus, while equity holders are interested in the

return over and above the amount which is required to repay debt. Debt holders are only

interested in the debt payment specified in the contract. Also, it is seen that most equity holders

are sometimes being interested in pursuing riskier business activities than debt holders would

prefer, when this occurs, debt holders may charge higher prices for debt capital and this

constitute greater control measures to prevent managers, from investing the capital in riskier

undertakings (Simerly and Mingfaing, 2000).

Sultz (1990) and Harris and Raviv (1990) provide further development to the agency model.

While Sultz’s work is on the hypothesis that the firm is in possession of important cash flows

generating abundant liquidity, thus supporting the idea of an optimal financial structure of

financing that would result from a compromise between benefits related to the reduction of cash

flows and the inconvenience that this cash flows may be so weak when investment opportunities

are good, Harris and Raviv approach their research problem under a different angle. They

estimated that conflicts between shareholders and mangers can result from disagreement in

optimal resource allocation. Thus Harris and Raviv op cit predict that firms with stronger

29

liquidity value and therefore with less cost of information are more likely to contract new debts.

This would lead them to rapidly experience failure thus favouring their control by investors.

A new approach, to testing the agency theory was studied by Allen and Wharton (2002).

According to them, agency costs represent important problems in corporate governance for both

financial and non-financial industries. They assumed that the agency theory suggest that the

choice of financing structure may help mitigate these agency costs. To them, under the agency

cost hypothesis, high leverage or a low equity/asset ratio reduces the agency cost of outside

equity and increases firm value by constraining or encouraging manages to act more in the

interest of shareholders (Allen and Wharton, 2002). Grossman and Hart (1982) and Williams,

(1987) were of the view that greater financial leverage may affect managers and thus reduce

agency costs through the threat of liquidation which causes personal losses to managers, loss of

salaries, low reputations, perquisites and through pressure generate cash flows to pay interest

expenses (Jensen, 1986).

For Dybrig and Douglas (1984), their contribution to Agency cost theory is presented in the form

of models in which managers have better information than investors but managers’ compensation

schemes are fine tuned to assure optimal capital investment. However Shivdasani (1993)

questions whether shareholders or board of directors could creditably commit to the optimal

compensation schemes that Dybrig and Zender had in mind. Other contributors to the Agency-

cost theories are Shlieifer and Vishney (1989); Berger, et al (1997); Lubatkin and Chattergee

(1994); Elliot (2002); Jensen and Ruback (1983); Spence and Zeckhauser (1971); Ross (1973);

Smith and Warner (1979); Holthansen and Leftwich (1983) among others.

While Shheifer and Vishney (1989) were of the opinion that Agency cost may make the

entrenchment of investment which adopt to firm’s assets and operations to the manager’s skills

and knowledge in order to increase the manager’s bargaining powers against investors, Berger, et

al (1997) found an inverse relationship between leverage and several measures of managerial

entrenchment and also found that events that ought to reduce the entrenchment generally lead to

increased leverage. Kaplan (1994) found that legal changes that protect firms from takeovers

30

leads to lower leverage while Lubatkin and Chattergee (1994) argued that increasing the debt to

equity ratio will help firms ensure that managers are running the business more efficiently. Elliot

and Elliot (2002) in supporting the agency cost theory say shareholders of a company are the true

owners and the duty of top management should be solely to ensure that shareholders interests are

met. In other words, the duty of top managers should be to manage the company in such a way

that returns to shareholders are maximized thereby increasing the profit figures and cash flows.

In trying to outline problems that exists between management and shareholders. Jensen and

Ruback (1983) said that manages use the excess free cash flows available to fulfil their own

personal interest instead of increasing returns to the shareholders.

Spence and Zechkauser (1971) and Ross (1973), provided formal analyses of the problems

associated with structuring the compensation of the agent to align with his or her incentive to the

interest of the principal. Smith and Warner (1979) provided detailed analyses of the monitoring

and bonding technology for control of the conflict of interest between bond holders and equity

holders demonstrating how observed bond contracts should vary in response to these agency

problems. Smith and Watts (1982) examined the control of the conflict between stockholders

and managers. They analyze the structure of management compensation contract focusing on the

trade-off between salaries, stock options, restricted stocks, bonus plans and other frequently

observed compensation provisions. Mayers and Smith (1982) analyzeed corporate insurance

purchases and argued that insurance contacts produce an efficient location of risk bearing and

provide an efficient administration of clauses against the corporation (Holthansen and Leftwich,

1983).

2.2.8 Overview of the Modigliani and Miller Theorem

The Modigliani and Miller irrelevance theory of the firm’s financial structure here refers to MM

forms the basis for modern thinking on capital structure (Arnold, 2007). Their theorem states that

under a certain market prices process in the absence of taxes, bankruptcy cost asymmetric

information and in an efficient market, the value of a firm is unaffected by how that firm is

financed (Modigliani and Miller, 1958). Accordingly, it does not matter if the firm’s capital is

31

raised by issuing stock or selling debt. It also does not matter what the firm’s dividend policy is

(Modigliani-Miller, 1961).

The theorem was originally proven under the assumption of no taxes. It is made up of two

propositions which can also be extended to a situation with taxes. Consider two firms which are

identical except for their financial structure. The first (firm U) is unleveraged. That is it is

financed by equity only. The other firm (firm L) is levered; it is financed partly by equity and

partly by debt. The Modigliani Miller theorem states that the value of the two firms is the same.

Without Taxes

Proposition 1: Vu = VL

Where Vu = is the value of an Unleveraged firm,

= the price of buying a firm composed only of equity.

VL = is the value of a Levered firm

= price of buying a firm that is composed of some mix of debt and

equity

To see why this should be true, suppose an investor is considering buying one of the two firms U

and L. instead of purchasing the shares of the levered from L, he/she could purchase the shares

of firm U and borrow the same amount of money from the bank that firm L does. The eventual

returns to either of these investments would be the same. Therefore, the price of L must be the

same as the price of U minus the money borrowed from the bank, which is the value of L’s debt.

Figure 2.2 MM Proposition 2

K Ke

Ko

Kd

Source: Pandey, (2005)

32

Proposition 2 is with risky debt. As leverage (D/E) increases, the WACC (ko) stays constant

Here Ke = ko + D (Ko – Kd)

E

Where Ke = required rate of return on equity

Ko = Cost of capital for an all equity firm

Kd = cost of debt

D/E = Debt-to-equity ratio

Therefore, a higher debt-to-equity ratio lead to a higher required return on equity, because of the

higher risk involved for equity holders in a company with debt. This formula is derived from the

theory of Weighted Average Cost of Capital (WACC) (Ezzell, 1980).

Proposition with Taxes

Proposition I: VL = VU + TcD

VL = Value of Levered firm

Vu = Value of Unlevered firm

TcD = Tax rate (Tc) x value of debt

This means that there are advantages for firms to be levered, since corporations can deduct

interest payments. Therefore, leverage lowers tax payments.

Proposition 2

RE = Ro + D (Ro – Rd) (1 – Tc)

E

Where

Re = cost of equity

Ro = Cost of capital for an all equity firm

Rd = cost of debt

D/E = Debt-to-equity ratio

Tc = Tax rate

33

The MM theorem is also called the financial structure ‘‘irrelevance principle”, “irrelevance

proposition” “neutrality proposition” or “the invariance proposition” (Pagano, 2005). In fact,

Pagano said the Modigliani and Miller (MM) theory was a cornerstone of finance for two

reasons. The first is substantive and stems from their nature of irrelevance proposition by

providing a crystal clear benchmark case where financial structure and dividend policy do not

affect the firm value, by implication, these proposition help us understand when these decisions

may affect the value of firms and why. As Pagano continued, the entire subsequent development

of corporate finance can be descried essentially as exploring the consequences of relaxing the

MM assumptions. The second reason for the seminal importance of MM according to him, was

methodological, thus by relying on an arbitrage argument, they set a precedent not only within

the realm of corporate finance but also an even more importantly within that of asset pricing.

As shown above with propositional formula, Modigliani and Miller produced two propositions,

the first concerning the irrelevance of the firm value to its financial structure (Gordon, 1989;

Modigliani and Miller, 1958) and the other concerning its irrelevance to dividend policy

(Modigliani and Miller 1963). But it is the first of these two propositions that has always

attracted the most of the attention, including even MM themselves. Indeed, as Pagano said, they

produced the dividend irrelevance proposition mainly to deflect criticism of their first position

(Pagano, 2005).

While the first MM theorem stated the conditions under which the choice between debt and

equity to finance a given level of investment does not affect the value of a firm, implying that

there is no optimal leverage ratio (MM, 1958; Pandey, 2005; Okafor, 1983; Gordon, 1989;

Pagano, 2005; Arnold, 2007; Gieseke and Goldberg, 2004; Rubinstein, 2003; Brealey, Myers

and Marcus, 2004). While the second MM theorem showed that under the same conditions

dividend policy does not affect a firms’ value, so there is no optimal payout ratio (MM, 1961;

Pandey, 2005; Pagano, 2005; Rubinstein, 2003). These in a nutshell, are theorems that show the

irrelevance of a choice that at first sight would seem very important such as the capital structure

decisions and the dividend decisions. In line with the above, the very words of Merton Miller

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witness that this was the main message of the MM theorem. When considering his work with

Franco Modigliani thirty years. Later, he stated;

…the view that capital structure is literally irrelevant or that “nothing

matters” in corporate finance though still sometimes, attributed to us is

far from what we actually said about the real world applications of our

theoretical propositions. Looking back now, perhaps we should have

put more emphasis on the other, more upbeat side of the nothing

matter’ coin, showing that what doesn’t matter can also show, by

implication what does… (Miller, 1988:10)

However, it must be said here that, the MM theorem have been a subject of enormous

controversy (Gieseke and Goldberg, 2004). Aspects of these controversy are examined by

Rubinstein (2003) who pointed out the statement and proof of an MM type result can be found in

William (1938), so, ab inito, MM were not the first to argue the irrelevance theorem, thus

according to him, not only does Williams result predate the famous paper of MM by 20 years, it

had a broader reach. For example, in their “no arbitrage” argument, MM (1958), which

computed the present value of the firm’s debt by discounting at a risk-free rate, thereby

neglecting firms that were subject to default?

The argument in William’s 1958 work does not suffer from this constraint (see, Gieseke and

Goldberg, 2004). Rubinstein (2003) concluded by looking backward to the MM theorem from

the perspective of modern finance. In fact, he identified a minimal set of axioms required for

MM to hold. These according to him are; there are no riskless arbitrage opportunities, operating

income (from assets) is not affected by capital structure, the proportion of operating income that

is jointly allocated to stocks and bonds is not affected by the firm’s capital structure and the

present value function, (the economy wide state price) is not affected by capital structure. These

four axioms cited by Rubinstein described an idealized economy. Therefore, the MM theorem

serves less as a statement that the leverage ratio is irrelevant to firm’s value than as a benchmark

35

from which to measure the ways in which leverage ratio affect firms’ value (Gieseke and

Goldberg, 2004).

Pagano (2005) was of the opinion that the MM theorem establishes that a company’s value that

is, the market value of its shares and debt is equal to the present discounted value of the

company’s cash flows, gross of interest, where the discount value is the required return for firms’

of the same “risk class”. Hence the firm value is determined solely by the discount rate and its

class flows, that is by its assets and it is wholly independent from the composition of the

liabilities used to finance the assets. The theorem according to him implies that the average cost

of capital is independent of the volume and structure of debt and it equals the return required by

investors for firms of the same “risk class”. Although, debt may appear cheaper than equity due

to the presence of a risk premium, increasing leverage does not reduce the average cost of capital

for the firm, because its effect would be precisely offset by the greater cost of equity capital. As

a result as Pagano (2005) continued, investment decisions can be totally decoupled from their

financing, they should be guided only by the criterion of maximizing the value of such

investment and the cost of capital to be used in rational investment decisions, that is, its total cost

measured by the required rate of return on fully equity financed firms of the same risk class.

However, despite the criticism level against the irrelevance theorem of MM, it must be said that

the entire development of corporate finance since 1958 (the publication date of the first MM

article) have been the cornerstone of finance, thus have generated a lot of interest among finance

scholars, however the assumptions upon which their theorem was based when subjected to real

life situations cannot hold. This has lead to the relaxation of three assumption of the MM

theorem (Pagano, 2005). First, the no tax-assumption was the first to be relaxed at the hand of

MM themselves, who recognized that the preferential treatment of debt by the U.S tax code

implied that an optional financial structure would require a larger leverage than those observed in

reality (MM, 1963). Much of the later work by MM according to Pagano (2005) and many others

were in refining this basic assumption, and studying how it should be modified to take into

account the differential taxation of interest income and capital gains at the personal level. In a

way though, the analysis led to a considerable downward revisions of the earlier MM

36

conclusions about the huge value increase that most U.S corporations could obtain by increasing

their leverage. Other writers according to Pagano (2005) went in a different direction to find an

offsetting cost to the tax advantage of debt and identified it in the cost of bankruptcy (Pandey,

2005), thereby relaxing the second MM assumption. Increasing leverage would bring value

increases in the form of tax benefits but would also raise the probability of incurring the cost of

bankruptcy (Brealey, et al, 2004). Under suitable assumptions, this could generate an interior

optimum, a value maximizing leverage that would equate the marginal benefit from tax savings

with the marginal cost from the increased likelihood of bankruptcy (Pagano, 2005). Thirdly, a

truly tidal flow of advances in corporate finance occurred by relaxing the third MM assumption

that of “friction less market” the most widely analyzed “friction” was that arising from

asymmetric information in financial market, that is, adverse selection and/or moral hazard

between external financiers and company managers (Sultz, 1990).

2.3 Empirical Review

2.3.1 Capital Structure of Firms

Since the seminar paper of Miller and Modigliani in 1958, the capital structure of firms has been

one of the most examined topics in finance and economic literature. For instance Lemmon and

Zender (2004) examined the impact of debt capacity on recent tests of competing theories of

capital structure. Controlling for debt capacity, the pecking order according to him appeared to

be a good description of financing behavior for a large sample of firms. Their main results reveal

that firstly, internally generated funds appeared to be the preferred source of financing for all

firms. Second, if external funds were required, in the absence of debt capacity concerns, debt

appeared to be preferred to equity. Concerns over debt capacity largely explain the use of new

external equity financing by publicly traded firms. Thirdly, when possible, debt capacity is

“stockpiled” they, thus provide evidence of the stockpiling of debt capacity by profitable, low

leverage firms that expect to use little external finance in the future. This evidence is directly

contrary to predictions of the tradeoff theory. Finally, they present evidence that reconciles the

frequent equity issues by small, high-growth firms with the pecking order.

37

Hancock (2009) investigated capital structure theories when capital is sourced through

investment by family and friends (F&F) in new venture start-ups. They stated that entrepreneurs

typically finance new ventures through self-financing, loans, bootstrapping, and equity

investment. About US$196 billion annually was sourced from F&F investors. Firms utilize

different forms of finance at different lifecycle stages. Capital structure theories were used to

explain how entrepreneurs choose the type and source of their finance at the different stages of

firms’ lifecycles. Contemporary research into early stage of equity finance primarily used capital

structure theories when examining informal business angel and formal venture capital (VC)

investors. F&F finance research using capital structure theory, however, is scanty.

Huang and Ritter (2008) examined time-series patterns of external financing decisions and

showed that publicly traded U.S. firms fund much larger proportion of their financing deficit

with external equity when the cost of equity capital is low. Their investigation revealed that the

historical values of the cost of equity capital have long-lasting effects on firms’ capital structure..

They introduced a new econometric technique to deal with biases in estimating the speed of

adjustment towards target leverage and found that firms adjust toward target leverage at a

moderate speed, with a half-life of 3.7 years for book leverage, even after controlling for the

traditional determinants of capital structure and firm fixed effects.

Chen and Chen (2011) posited that pecking order theory of capital structure was one of the most

influential theories of corporate finance. The purpose of their study was to explore the most

important factors on a firm’s capital structure by pecking-order theory. Hierarchical regression is

used as the analysis model. This study examined the determinants of debt decisions for 305

Taiwan electronic companies that were quoted on the Taiwan Stock Exchange of 2009. The

results indicated that profitability which is a determinant of capital structure negatively affects on

capital structure. It implies that firms prefer to use their earnings to finance business activities

and thus use less debt capital. Growth rate positively affects to capital structure. The greater

growth opportunities are the more capital structure to finance the growth. Size were moderator

variable in this study. Size of firms moderates the effects of tax rate on capital structure. Large

38

firms appear to take advantage of the tax deductibility of debt. The findings are important for

management and investors.

Huang and Ritter (2004) examined time-series patterns of external financing decisions.

Consistent with the market timing theory of capital structure, publicly traded U.S. firms fund a

much larger proportion of their financing deficit with net external equity when the expected

equity risk premium is lower, they reported that the first-day returns of initial public offerings

were higher, and prior (post) realizations of the Fama-French value factor were lower (higher).

The result was inconsistent with the pecking order theory.

Tayo (2012) posited that the ongoing adjustment and reform efforts of Nigeria, and the recent

crisis in the nations’ capital market, had made known the importance of finding optimal

adjustment path that will maximize the inter-temporal social welfare function of the country,

subject to capital structure constraints. He examined speed of adjustment of Nigeria Listed firms

to target capital structure. This study made use of panel data from secondary sources collated

mainly from annual financial statements and reports of sampled companies quoted on the

Nigerian Stock Exchange (NSE) over a study period of 10 years covering 2000-2009. Samples of

85 nonfinancial manufacturing listed companies were purposively selected for analysis. The

findings of the study showed that firms adjust toward target leverage at a moderate speed, with a

half-life of 3.9 years for book leverage, even after controlling for the determinants of capital

structure and firm fixed effects. However, if projects appeared with much higher frequency, and

if they needed to be financed quickly, even this adjustment seemed slow.

Myers (2002) evaluated the four major theories of corporate financing: (1) the Modigliani-Miller

theory of capital-structure irrelevance, in which firm values and real investment decisions were

unaffected by financing; (2) the trade-off theory, in which firms balance the tax advantages of

borrowing against the costs of financial distress; (3) agency theories, in which financing

responded to managers’ personal incentives, and (4) the pecking-order theory, in which financing

adapts to mitigate problems created by differences in information. He argued that these theories

were conditional, not general. He surmised that firms with high profitability and valuable growth

39

opportunities tend to borrow less. Each of these tendencies is consistent with two or more of the

major theories of financing. It may be possible to devise sharper tests by exporting the theories to

developing economies, where agency and information problems are more severe. Further

progress in understanding corporate financing decisions will require a deeper understanding of

agency issues when value-maximizing operating and investment decisions cannot be observed or

verified. But managers are not just temporary agents motivated by immediate pecuniary

compensation or perquisites.

Strebulaev (2007) were of the opinion that presence of frictions, firms adjust their capital

structure infrequently. As a consequence, in a dynamic economy the leverage of most firms is

likely to differ from the “optimum” leverage at the time of readjustment. He explored the

empirical implications of this observation and used a calibrated dynamic trade-off model to

simulate firms’ capital structure paths. The results of standard cross-sectional tests on these data

were consistent with those reported in the empirical literature. In particular, the standard

interpretations of some test results lead to the rejection of the underlying model. Taken together,

the results suggested a rethinking of the way capital structure tests were conducted.

Prasad, Green and Murinde (2001) critically surveyed the key literature on corporate financing

policy, capital structure and firm ownership in order to identify the leading theoretical and

empirical issues in these areas. The theoretical component of the survey attempted to reconcile

competing theories of capital structure and appraised recent models which used agency theory

and asymmetric information to explore the impact of managerial shareholdings, corporate

strategy and taxation on the firm’s capital structure. The empirical component focused on

univariate analyses as well as multivariate models of capital structure, and made a comparison

between theoretical predictions and empirical results.

Buhr, et al (2005) examined capital structure theory and how it relates to a firm’s financing

choices. They used a modified pecking order framework to analyse financing choices for

Australian firms. The traditional pecking order model has been extended to allow a non-linear

relationship between a firm’s requirements for external capital (the financial deficit) and the

40

amount of external debt used to meet these requirements. The pecking order theory predicts that

firms will follow a defined hierarchy of financing choices with internal funds being used first,

followed by external debt and as a last resort the issuance of external equity. Their main finding

is that Australian firms do not follow the pecking order as closely as in other markets as the

model explains less of the variation in debt issuance. Importantly, They also found that this is not

related to debt capacity constraints, which has been hypothesized by other researchers as a

legitimate reason why firms, small firms in particular, would not appear to be following the

pecking order theory.

Buhr et al (2005) used Altman’s Z-Score, which is a commonly used measure of financial

distress, to identify firms that are relatively unconstrained in terms of debt capacity. They also

found that while controlling for debt, improvement is only marginal. They did not find evidence

against the static trade-off theory of capital structure. In particular firms that were unconstrained

in terms of debt capacity and not facing significant capital expenditure did not increase leverage

towards an optimal capital structure in the manner predicted by the static trade-off theory. They

hypothesized that at least part of the reason for these findings was due to taxation differences,

with the imputation credit system in Australia effectively removing the tax advantage of debt for

domestic investors. Another important factor that could explain the lower explanatory power of

the pecking order model could be the more accepted use of warrants and rights issues to raise

equity, which have been argued to have lower asymmetric information costs than issuing straight

equity.

Myers (2003) contrasted the "static tradeoff" and "pecking order" theories of capital structure

choice by corporations. In the static tradeoff theory, optimal capital structure was reached when

the tax advantage to borrowing was balanced, at the margin, by costs of financial distress. In the

pecking order theory, firms preferred internal to external funds and debt to equity if external

funds were needed. Thus the debt ratio reflected the cumulative requirement for external

financing. The paper closed with a review of empirical evidence relevant to the two theories.

41

Jong et al (2005) tested the static tradeoff theory against the pecking order theory. They

measured firms’ target leverage and debt capacity in order to discriminate between the theories

and when leverage exceeded the target and below the debt capacity, static tradeoff predicted a

decrease in leverage. They found that the pecking order theory was a better descriptor of firms’

financing and repurchasing behavior than the static tradeoff theory. They found firms to be

consistent over time in their preference for a specific capital structure theory.

Fohlin (1998) opined that the pecking order theories predict that information asymmetries result

in excess costs of, and thus resistance to, outside versus inside finance. He opined that bank

relationships should ameliorate information problems, reduce cost differentials, and diminish

reliance on internal funds and bank debt. Thus, he supported the pecking order hypothesis

generally but found little static effect of bank oversight on firms’ capital structure or use of bank

debt. The findings cast doubt on the standard perception of interlocking directorates as an

important source of information or signals of quality.

Ahmadinia, et al (2010) provided a comprehensive review on different theories and hypothesis in

regard to achieving an optimal capital structure. They opined that many researchers believed that

capital structure includes share issuance, private investment, bank debt, business debts, leasing

contracts, tax debt, retirement debt, deferred compensation for executives and employees,

deposits, product related-debt and other probable debt. According to them, by applying these

theories, the analysts will be able to reach a maximum return with minimum risk while they

increase the value of corporation because of the close relationship between profitability and

capital structure. Their study suggested a new model called genetic algorithm model by using

support vector regression and profitability factors for obtaining an international range of optimal

capital structure.

Miglo (2011) surveyed 4 major capital structure theories: trade-off, pecking order, signaling and

market timing. For each theory, a basic model and its major implications were presented and

compared to the available evidence. This was followed by an overview of pros and cons for each

theory.

42

Lewellen and Lewellen (2005) argued that trade-off theory’s simple distinction between debt and

‘equity’ was fundamentally incomplete because firms have three, not two, distinct sources of

funds: debt, internal equity, and external equity. Internal equity (retained earnings) generally

should be less costly than external equity for tax reasons, and may even be cheaper than debt. It

followed that, without any information problems or adjustment costs, optimal leverage would be

a function of internal cashflows. Debt ratios could wander around without a specific target, and a

firm’s cost of capital should depend on its mix of internal and external finance, not just its mix of

debt and equity. The trade-off between debt, retained earnings, and external equity should

depend critically on the tax basis of investors’ shares relative to current price.

Frank and Goyal (2002) tested the pecking order theory of corporate leverage on a broad cross-

section of publicly traded American firms for 1971 to 1998. Contrary to the pecking order

theory, net equity issues tracked the financing deficit more closely than did net debt issues.

While large firms exhibited some aspects of pecking order behavior, the evidence was not robust

to the inclusion of conventional leverage factors, nor to the analysis of evidence from the 1990s.

Financing deficit was less important in explaining net debt issues over time for firms of all sizes.

Bulan and Yan (2009) examined the central prediction of the pecking order theory of financing

among firms in two distinct life cycle stages, namely growth and maturity. They found that

within a life cycle stage, where levels of debt capacity and external financing need were more

homogeneous, and after sufficiently controlling for debt capacity constraints, firms with high

adverse selection costs followed the pecking order more closely, consistent with the theory.

Meier and Tarhan (2009) were of the view that a number of studies test the pecking order

hypothesis. However, the empirical model used suffers from some specification issues. They

conducted a survey of 127 CFOs and found that on average they followed the precise financing

sequence predicted by the theory. However, when they estimated the empirical model for the

survey firms, as in Frank and Goyal (2003), they found little support for the pecking order

hypothesis. Furthermore, testing pecking order by controlling for debt capacity Lemmon and

43

Zender (2009) does not qualitatively change the results and finally suggest that future research

need to address the contradictory conclusions of regression based tests.

Agca and Mozumdar (2004) were of the opinion that the relative importance of internal cash,

new debt, and new equity in the aggregate financing mix of public firms was as predicted by the

pecking order theory and suggested that recent evidence to the contrary was due to scaling by

firm size and use of equal-weighted estimators. The poor performance of the pecking order

theory for small firms was due to the impact of debt capacity: small firms had low debt

capacities which were quickly exhausted, forcing them to issue equity. The pecking order theory

performed satisfactorily for large firms especially firms with rated debt, and when the impact of

debt capacity was accounted for. Consistent with the theory, the debt-deficit relationship was

found to be concave and piecewise linear with slopes close to predicted values of 1 and 0.

Leary and Robert (2004) empirically examined the pecking order theory of capital structure,

while accounting for the value of financial slack. They began by developing an empirical model

that was motivated by the pecking order's decision rule and implied financing hierarchy. The

model address the statistical power problem associated with previous empirical tests that enabled

them to identify those decisions that conformed to and those that violated the theory's

predictions. They found that the pecking order was unable to explain why firms turn to external

capital markets and, conditional on using external funds, why firms chose to issue equity. Of the

firm-year observations where firms used external finance (equity), less than 40% were consistent

with the pecking order's prediction. Thus, firms violate the financing hierarchy more often than

not and these violations were due neither to time varying adverse selection costs or debt capacity

concerns. When compared to a sample of private borrowers for which had detailed loan and

firm-characteristic information, the majority of equity issuers were not materially different from

their counterparts that turned to the private debt market.

2.3.2 Capital Structure and Firm Growth

Examining the impact of Capital structure and firm growth, Bulan and Sanyal (2009)

investigated the impact of growth opportunities on the financing decisions of investor-owned

44

electric utilities in the U.S. when the electricity sector was deregulated. They found that the

relationship between leverage and growth opportunities could be positive or negative, depending

on the nature of the growth opportunity. Despite these opposing effects on leverage ratios, they

found that issuing new debt is due largely to response for growth opportunities. Their results

highlight that financing choice is not a simple one-period decision but a dynamic occurrence and

that conventional leverage regressions could not fully capture this dynamic response.

Saeedi and Mahmoodi (2010) investigated the determinants of capital structure of Iranian firms

listed in the Tehran Stock Exchange. The investigation was performed using Generalized

Method of Moment (GMM) approach for 146 listed firms in the Tehran Stock Exchange over the

period 2003 to 2008. This study employed two alternative leverage measures (including book

leverage and market leverage) as dependent variables and seven factors (including profitability,

growth opportunity, liquidity, business risk, effective tax rate, size and tangibility) as

determinants of capital structure. The results indicated that leverage decreased with profitability,

liquidity and tangibility while increased with business risk. There was no significant relationship

between leverage and effective tax rate. Moreover, the results showed that firm size had a

positive relationship with market leverage and a negative relationship with book leverage.

Furthermore, their findings indicated that growth opportunity was positively related to market

leverage, but by contrast, growth opportunity was negatively related to book leverage. Finally,

their results indicated that both trade-off and pecking order theories could explain financing

decisions of Iranian firms. In the other words, none of these theories could be rejected.

Frielinghaus et al (2005) argued the case for a relationship between capital structure and a firm’s

life stage. They provided an overview of the two sets of theories and followed this with a

proposed linkage between the life stage and capital structure. They used the Adizes life stage

model to assess the life stage of the firms in their sample. Their pilot study found a statistically

significant relationship between life stage and the capital structure of the respondents. The nature

of the relationship (more debt in the early and late life stages than in prime) supported the

pecking order theory of capital structure and suggested a practical use of the life stage model in

helping firms to understand how their financing was likely to change over time.

45

Mayer and Sussman (2002) reported a new test of capital structure theories using a filtering

technique to identify large investment projects. Contrary to the results of aggregate studies, firms

responded to investment spikes by raising large amounts of external finance. Large firms raised

debt finance and small firms issued new equity. These results ran counter to predictions of the

pecking order theory. New equity did not come higher up the pecking order either in relation to

specific investments or over the life cycle of firms. They also rejected a static version of the

trade-off theory by which the financing of new investment was determined by invariant

characteristics of firms. However, their findings supported a dynamic version of the theory in

which firms adjusted to target levels of leverage both during and after investment projects. They

provided evidence that the reason why dynamic rather than static version of the theory prevailed

was that firms faced constraints in their choice of finance.

Boodhoo (2009) presented empirical findings in support of the main theories developed on

capital structure and its determinants, and on the impact of debt ratio on firm’s performance.

Empirical results based on 2002 to 2006 accounting data for 40 Mauritian firms were consistent

with past literature on the topic, and implied that the agency costs, tax rate, capital expenditures

and the ownership structure played a fundamental role in financing decision. Unexpectedly,

performance and tangibility, which had been extensively considered as important determinants in

financing decision, were not statistically significant to the current model. The result also

provided additional support to the hypothesis of the existence of an optimal debt ratio, which

balanced the tax deductions gains from high leverage with the additional expenses that it

implied, namely the cost of servicing the debt, and all the costs related to the increased risk of

financial distress and bankruptcy. Taken as a whole entity, the optimal capital structure for

Mauritian firms analysed ranges somewhere around 50 percent, within which the marginal

benefits derived from leverage were equal to the marginal costs.

Hrdy and Marek (2008) analyzed the theoretical and practical problems concerning optimizing of

the capital structure of the concrete firm and to answer the question if it was possible to prepare

the recommended process for this optimizing. The most important problem in a theoretical way

46

was to identify the theory which best fit. The firm had to decide firstly if to start the process of

active optimizing or if it was satisfied with the following of branch standards or it was satisfied

with complying with the pecking order theory. In case of active optimizing process firm could

use theories stipulating the concrete empirical value, the traditional theory and the theory of

spouses Neumaiers’. In case of using the traditional theory it was necessary to cope with the

problems of identifying the cost of equity and debt in dependency on the indebtedness. The

optimal capital structure was not possible to identify, but only to estimate because of the

different approaches to the solution of the application problems of single theories. The optimal

capital structure would also vary because of the subjective approach to the process of optimizing

returns. Nevertheless the wider manual how to cope with the process of the optimizing of the

capital structure of the concrete firm was possible to prepare.

Eriotis, et al (2010) examined firm characteristics that affect capital structure. The study was

performed using panel data procedure for a sample of 129 Greek companies listed on the Athens

Stock Exchange during 1997- 2001. The number of the companies in the sample corresponded to

the 63 per cent of the listed firms in 1996. The firm characteristics were analyzed as

determinants of capital structure according to different explanatory theories. The hypothesis that

was tested in this study was, “the debt ratio at time t depends on the size of the firm at time t, the

growth of the firm at time t, its quick ratio at time t and its interest coverage ratio at time t”. The

firms that maintained a debt ratio above 50 per cent using a dummy variable were also

distinguished. The findings of this study justified the hypothesis that there was a negative

relation between the debt ratio of the firms and their growth, their quick ratio and their interest

coverage ratio. Size appeared to maintain a positive relation and according to the dummy

variable there was a differentiation in the capital structure among the firms with a debt ratio

greater than 50 per cent and those with a debt ratio lower than 50 per cent. The study was able to

prove that financial theory does provide some help in understanding how the chosen financing

mix affects the firm’s value.

47

Khan (2010) explored the relationship of capital structure decision with the performance of the

firms in the developing market economies like Pakistan. The Pooled Ordinary Least Square

regression was applied to 36 engineering sector firms in Pakistani market listed on the Karachi

Stock Exchange (KSE) during the period 2003-2009. The results showed that financial leverage

measured by short term debt to total assets (STDTA) and total debt to total assets (TDTA) had a

significantly negative relationship with the firm performance measured by Return on Assets

(ROA), Gross Profit Margin (GM) and Tobin’s Q. The relationship between financial leverage

and firm performance measured by the return on equity (ROE) was negative but insignificant.

Asset size had an insignificant relationship with the firm performance measured by ROA and

GM but negative and significant relationship existed with Tobin’s Q. Firms in the engineering

sector of Pakistan were largely dependent on short term debt but debts were attached with strong

covenants which affected the performance of the firm.

Raheman et al (2007) were of the opinion that capital Structure referred to the various financing

options of the assets by a firm. A business concern could go for different levels of the mixtures

of equity, debt and/or other financial facilities with equity having the emphasis on maximizing

the firm’s value. Capital Structure affected the liquidity and profitability of a firm. In their

research they had tried to examine the effect of capital structure on the profitability of firms

listed on Islamabad Stock Exchange. In this regard they selected a sample of 94 non financial

firms for a period of six years from 1999 – 2004. The data was collected from the financial

statements (Annual Reports) of these 94 non financial firms. For analysis purpose, they used

Pearson’s correlation, and regression analysis. Pooled ordinary least square model was used in

the estimation of a function relating to the net operating profitability with the independent

variables including debt ratio, long term debt to liabilities, equity to liabilities and size of the

firm measured in terms of natural logarithm of sales. The results indicated that the capital

structure of the non financial firms listed on Islamabad Stock Exchange had significant effect on

the profitability of these firms. If these firm wanted to increase their profitability, they would had

to give due consideration to the financing mix, otherwise it would suffered from losses.

48

Abor (2008) compared the capital structures of publicly quoted firms, large unquoted firms, and

small and medium enterprises (SMEs) in Ghana. Using a panel regression model, they examined

the determinants of capital structure decisions among the three sample groups. His results

showed that quoted and large unquoted firms exhibited significantly higher debt ratios than did

SMEs. The results did not show significant difference between the capital structures of publicly

quoted firms and large unquoted firms. The results revealed that short-term debt constituted a

relatively high proportion of total debt of all the sample groups. The regression results indicated

that age of the firm, size of the firm, asset structure, profitability, risk and managerial ownership

were important in influenced on the capital structure decisions of Ghanaian firms. For the SME

sample, it was found that factors such as the gender of the entrepreneur, export status, industry,

location of the firm and form of business were also important in explaining the capital structure

choice. The study provided useful recommendations for policy direction and management of

these firms.

Kayhan (2008) examined the effect of managerial discretion on capital structure dynamics.

Analyses of financing decisions indicated that managers with more discretion preferred issuing

equity over debt. Examination of leverage changes suggested that increases in debt ratios due to

positive and negative financial deficits were greater for managers with high discretion.

Furthermore, when managers had high discretion, debt changes seemed to be more sensitive to

issuance activities than to repurchase activities. For high‐discretion managers, market timing

activities (equity issuance following increases in stock prices) and the passive response to stock

price appreciations, resulted in greater declines in debt ratios. Finally, while firms tended to

rebalance their capital structures over time regardless of the level of managerial discretion, the

speed of target adjustment was much slower for high‐discretion managers.

2.3.3 Determinant of Capital Structure

Abor (2008) compared the capital structures of publicly quoted firms, large unquoted firms, and

small and medium enterprises (SMEs) in Ghana. Using a panel regression model, they also

examined the determinants of capital structure decisions among the three sample groups. The

results showed that quoted and large unquoted firms exhibit significantly higher debt ratios than

49

do SMEs. The results did not show significant difference between the capital structures of

publicly quoted firms and large unquoted firms. The results revealed that short-term debt

constituted a relatively high proportion of total debt of all the sample groups. The regression

results indicated that age of the firm, size of the firm, asset structure, profitability, risk and

managerial ownership were important in influencing the capital structure decisions of Ghanaian

firms. For the SME sample, it was found that factors such as the gender of the entrepreneur,

export status, industry, location of the firm and form of business were also important in

explaining the capital structure choice. The study provided useful recommendations for policy

direction and management of these firms.

Hassan (2011) investigated the determinants of capital structure in Nigerian listed insurance

firms using data obtained from annual report of the sampled firms for the period 2001-2010. He

used five explanatory variables to measure their effects on debt ratio. Multiple regressions were

employed as tool of analysis. The result revealed that all the explanatory variables had

statistically and significantly influenced the explained variable. The results approve the

prediction of pecking order theory in the case of profitability and trade-off theory in case of

tangibility variables. The growth variable supported the agency theory hypothesis whereas size

variable confirmed to the asymmetry of information theory. It was therefore recommended that

the management of listed insurance firms in Nigeria should always consider their position using

these capital structure determinants as important inputs before embarking on debt financing

decision.

Bannier and Grote (2008) examined the financing structure of small and medium-sized

enterprises (SMEs) in Germany and questions whether an equity gap – or, more generally, a

financing gap - existed. Reviewing the literature and available data sources, they found that

financing constraints seemed to affect, if at all, only a very small subgroup among highly

growth-oriented firms. They did not detect any structural problems in average SME’s capital

structure. Rather, German Mittelstand firms appeared to be non-growth oriented and content with

their financing decisions. While the relationship-based German banking system helped to

minimize the risk of credit rationing, trade credit offered an additional, stable form of liquidity.

50

Highly innovative firms with strong growth potential, on the other hand, did seize the

opportunity to tap unconventional means of financing (e.g. mezzanine capital) and appeared very

successful in doing so.

Shah and Hijazi (2004) attempted to answer the question of what determines the capital structure

of Pakistani listed firms other than those in financial sector. They used total debt ratio divided by

total assets as a proxy for leverage. They used four independent variables to measure their effect

on leverage. Their results showed that assets tangibility was positively correlated with debt;

however, this relationship was not statistically significant. They concluded that asset structure

did not matter in determination of capital structure of Pakistani firms.

Fakher, et al (2008) provided further evidence of the capital structure theories pertaining to a

developing country and examined the impact of the lack of a secondary capital market by

analysing a capital structure question with reference to the Libyan business environment. The

results of cross-sectional OLS regression showed that both static trade-off theory and agency

cost theory were pertinent theories to the Libyan companies’ capital structure whereas there was

little evidence to support the asymmetric information theory. The lack of a secondary market

may have an impact on agency costs, as shareholders who were unable to offload their shares

might exert pressure on management to act in their best interests.

Fattouh, et al (2004) developed a model of the firm’s maximization programme in which the

firm’s capital structure was a non linear function of a vector of costs including asymmetric

information costs and was subject to a debt ceiling. Using conditional quantile regression

methods, They tested for the existence of such a non- linearity in a heterogeneous sample of UK

firms and demonstrated that, by exploiting more fully the distribution of leverage, this technique

yielded new insights into the choice of leverage ratio. Not only was the estimated effect of the

explanatory variables different at different quantiles of the distribution, they found evidence that

the effect of a variable changed sign between low leveraged and high leveraged firms.

51

Sharif, et al (2012) investigated factors in developed countries which were attributed as

imperative ones to attain optimal capital structure, provided compelling justifications for capital

structure decisions in insurance companies of Pakistan. Empirical exploration of factors, that

drived optimal capital structure applied on panel data of 31 insurance firms from 2004 to 2009.

Two econometric panel data techniques, fixed effects and random effects were pertained.

Hausman’s specification test was performed in order to test appropriate model for the study. The

outcomes of study advocated that, profitability, age and earnings volatility had inverse relation

with leverage and was significant. Liquidity also had inverse relationship with debt ratio but it

was not significant. Alternatively, size and growth opportunities had direct relationship with

leverage but only size was significant. These outcomes were in line with theoretical theories such

as pecking order theory and trade off theory. This research had provided an initial foundation to

ascertain the factors influencing decisions of capital structure of Pakistan’s insurance sector and

it could be a preliminary base for more methodical investigation. Moreover, this could also be

helpful for the managers in making decisions about optimal capital structure. This study, to the

author’s knowledge, was conducted first time in Pakistan for investigating the capital structure

theories and their implications on insurance companies of Pakistan with the most recent panel

data available. Furthermore, this study validated that some features had an effect on capital

structure of Pakistani insurance companies as acknowledged in developed countries.

Ajao and Inyang (2012) examined directly detailed background information of manufacturing

sector in Nigeria with the aim of discovering major determinants of its capital structure. And the

basic determinants of capital structure in the firms identified by various studies were tangibility,

size, growth opportunities, profitability and non-debt In addition to these, issues such as

corruption, political atmosphere, nature of financial markets, had also been identified as

influencing seriously the capital structure of firms in Nigeria. The paper also highlighted issues

such as financial distress, bankruptcy threats, solvency problem, risk of default etc due to

unstable economic and political situations as possible dangers may plagued firms whose capital

structure tilted more towards debt financing.

52

Sbeiti (2010) investigated the determinants of capital structure in the context of three GCC

countries and the impact of their stock markets' development on the financing choices of firms

operating in these markets and the study adopted the approach of combining the dynamism of

capital structure and the impact of stock market development on firms financing choices. The

GCC countries were non-tax paying entities which made them an interesting case to investigate

whether the determinants of the capital structure of firms operating in these markets were similar

to those operating in the developed and industrial countries. Also, there was no single published

study which examined and compared the capital structure of firms listed in the GCC stock

markets or the stock markets development and firms financing choice in these countries. His

results revealed that (1) corporate capital structure in these countries could be explained by the

determinants suggested in corporate finance models, (2) stock markets in the these countries had

become more developed and considered an important tool for corporate financing decisions.

Chen and Jiang, (2001) investigated the financing behaviour of Dutch firms by testing whether a

firm’s financing decisions were determined by certain factors identified in various theories.

Since a firm’s financing decision was reflected in the changes of its leverage, their research

focused on the relationship between a firm’s debt ratio change and the changes in certain factors.

The approach used in the paper was the structural equation modeling (SEM) technique. The

model identified various important factors that were related to Dutch firms’ financing decisions.

The empirical results provided moderate support for the static trade-off theory, the pecking-order

hypothesis, as well as the dynamic capital structure model. However, their data set was

insufficient to confirm the static trade-off theory, and their results provided little evidence to

back the asymmetric information argument behind the pecking-order hypothesis.

Roberts and Sufi (2007) showed that a large number of financing decisions of solvent firms were

dictated by creditors, who used the transfer of control rights accompanying financial covenant

violations to address incentive conflicts between managers and investors. After showing that

financial covenant violations occurred among almost one third of all publicly listed firms, they

found that creditors uses the threat of accelerating the loan to reduce net debt issuing activity by

over 2% of assets per annum immediately following a covenant violation. Further, this decline

53

was persistent in that net debt issuing activity failed to return to pre-violation levels even after

two years, resulting in a gradual decline in leverage of almost 3%. These findings represented the

first piece of empirical evidence highlighting the role of control rights in shaping corporate

financial policies outside of bankruptcy.

Huang and Song (2007) employed a new database, which contained the market and accounting

data from more than 1000 Chinese listed companies up to the year 2000, to document the

characteristics of these firms in terms of capital structure. As in other countries, leverage in

Chinese firms increased with firm size, non-debt tax shields and fixed assets, and decreased with

profitability but correlates with industries. They also found that ownership structure affected

leverage. Different from those in other countries, leverage in Chinese firms increases with

volatility and firms tended to have much lower long-term debt. The static tradeoff model rather

than pecking order hypothesis seemed better in explaining the features of capital structure for

Chinese listed companies.

Beattie, Goodacre and Thomson (2006) argued that despite theoretical developments in recent

years, our understanding of corporate capital structure remained incomplete. Prior empirical

research had been dominated by archival regression studies which were limited in their ability to

fully reflect the diversity found in practice. The present paper reports on a comprehensive survey

of corporate financing decision-making in UK listed companies. A key finding was that firms

were heterogeneous in their capital structure policies. About half of the firms sought to maintain

a target debt level, consistent with trade-off theory, but 60% claim to follow a financing

hierarchy, consistent with pecking order theory. These two theories were not viewed by

respondents as either mutually exclusive or exhaustive. Many of the theoretical determinants of

debt levels were widely accepted by respondents, in particular the importance of interest tax

shield, financial distress, agency costs and also, at least implicitly, information asymmetry.

Results also indicate that cross-country institutional differences had a significant impact on

financial decisions.

54

Shah (2007) presented evidence that the capital structure of a firm was often a combination of

several securities; it could arrange for (1) Bank loan (2) issue debentures/bonds, (3) issue shares

(4) lease financing, or (5) utilised its retained earnings. Eventually number of ideas and theories

had been developed to discuss the optimal capital structure of firms, optimum capital structure

implies the trade-off between the benefit of tax and costs of financial distress; a firm could face

due to the borrowed money.

Although extensive research work had been done on the capital structure but still it remained one

of the unsettled topics in finance. Optimal capital structure had an impact on corporate profits.

Debt was considered as the cheapest source of financing due to tax shield, the higher the firm’s

tax bracket, the more the debt is advantageous to a firm. The trade off theory states that higher

debt is associated with higher profitability.

Three reasons support this theory; one debt allow tax shield. Second, more trust is built on

profitable companies considering more sustainable and less prone to bankruptcy; hence high

profitable companies are able to seek more debt. Third, agency cost, for the profitable firms,

lenders/creditors give relaxation in monitoring charges, which reduces the debt cost. This

motivates profitable firms to go for more debt. If firms follow pecking order theory then it bases

its financial decision on the availability of internally generated funds. While, profitable firms

prefer internal financing, external finance is only used when internally generated funds are not

sufficient.

Agarwal and O’Hara (2006) investigated the effects of information asymmetry across equity

investor groups as an explanation for the capital structure decisions of the firm. They tested

empirically whether differences in information across outside investors had any bearing on the

leverage ratios of firms and on their choice of financing instrument when raising external capital.

They used the probability of information-based trading (PIN) and other microstructure-based

proxies to test our theory and found that firms with higher information risk (extrinsic information

asymmetry across groups of investors) measured using PIN tended to have higher market

leverage. Extrinsic information asymmetry also seemed to play a significant role in the firm’s

55

decision to issue debt or equity when raising capital, with high PIN firms more likely to issue

debt. Looking at firms that issued debt and repurchased equity (increased leverage), or issued

equity and repurchased debt (decreased leverage) in the same year, they found that firms with

higher extrinsic information asymmetry were more likely to increase their leverage. These results

strongly supported the hypothesis that information risk affected capital structure after controlling

for information asymmetry between firm managers and outside investors.

Hovakimian et al (2001) examined whether market and operating performance affected

corporate financing behavior because they were related to target leverage. Their focus was on

firms that issued both debt and equity enhanced their ability to draw inferences. Consistent with

dynamic tradeoff theories, dual issuers offset the deviation from the target resulting from

accumulation of earnings and losses. Their results also implied that high market-to-book firms

had low target debt ratios. On the other hand, consistent with market timing, high stock returns

increased the probability of equity issuance, but had no effect on target leverage.

Saeed (2007) tested the impact of financial patterns of listed firms in energy sector of Pakistan

followed any foremost capital structure theories. The analysis was implemented on a sample of

22 listed firms during the period 2001 to 2005. The results of pooled regression model showed

that both Static trade-off theory and Pecking order theory were pertinent corporate capital

structure theories to the firms in Pakistani energy sector.

Ahmad et al (2011) tried to determine the influence of set of explanatory variables on the capital

structure determination for Pakistani non-financial firms by using panel data. This study also

found the applicability of two capital structure theories (pecking order theory and trade-off

theory) in Pakistani non-financial sector. This study used five previously studied variables

(profitability, size, growth, tangibility of assets, non-debt tax shield), and added three new

variables (tax, liquidity and payout), which were not used previously in Pakistani context. This

research used data from 336 non-financial firms over the period of 5 years (2005-2009). This

study used fixed effect random model regression analysis to analyze determinants of capital

structure. The results showed that industry type played important role in determining capital

56

structure. The results showed that out of eight variables five (size, tangibility of assets, non-debt

tax shields, liquidity and payout) were statistically significantly related to leverage, remaining

three were statistically insignificantly related with leverage. Two expected relation were

accepted while six were rejected after empirical analysis. This study identified that industry type,

liquidity and payout ratio played important role, whereas tax did not play important role in

identifying capital structure Pakistani non-financial firms.

Titman and Wessels (1988) analyzed the explanatory power of some of the recent theories of

optimal capital structure. The study extended empirical work on capital structure theory in three

ways. First, it examined a much broader set of capital structure theories, many of which had not

previously been analyzed empirically. Second, since the theories had different empirical

implications in regard to different types of debt instruments, the authors analyze measures of

short-term, long-term, and convertible debt rather than an aggregate measure of total debt. Third,

the study used a factor-analytic technique that mitigated the measurement problems encountered

when working with proxy variables.

2.3.4 Financing Choices of Firms

Firms rely on external financing as sources of funding. As proved by Robb and Robinson (2008)

who investigated the capital structure choices that firms make in their initial year of operations,

using restricted-access data from the Kauffman Firm Survey. Contrary to many accounts of

startup activity, the firms in the data relied heavily on external debt sources, such as bank

financing, and less heavily on friends and family-based funding sources. This striking fact held

even when they purged each firm’s credit score of variation due to demand-side credit

characteristics. The heavy reliance on external debt underscored the importance of well

functioning credit markets for the success of nascent business activity.

Sheehan and Graham (2001) examined the capital structure choices of high tech firms in the last

decade and how these choices related to current capital structure theory. This theory included the

Static Trade Off Theory and the Pecking Order Theory; the former held that firms made funding

choices as a function of the firm’s overall weighted average cost of capital and sought primarily

57

to minimize this cost of capital; the latter suggested that managers were loath to issue new

equity, derived funds first from internal sources such as earnings, second from debt and finally

from equity and equity-type issues. They found that high tech firms in the 1990s supported, in

some ways, the Static Trade Off suggestion that firms with strong and riskier growth options

hold more cash that other firms and were more likely to draw initial funding from the equity

markets. They found also that the market conditions of the 1990s allowed newer, smaller and

riskier firms’ greater access to equity markets than ever before as a means of building large cash

reserves. To account for this, they proposed an extension of existing Pecking Order Theory and

introduced a “Pecking Order Scale”. By viewing the key factors that influenced capital structure

choice as a continuum or scale from all equity on the left to all debt on the right, they were able

to portray the capital structure choice of the firm based on individual firm, industry and overall

market factors.

Coleman and Robb (2011) examined the financing strategies of startup firms included in the

Kauffman Firm Survey with a focus on the financing strategies of new technology-based firms.

Their findings support the Pecking Order and Life Cycle theories, at least in the case of new

technology-based firms. Their results revealed that technology-based firms used a higher ratio of

owner provided financing and lower ratios of financing from other insiders or external debt than

all firms during their startup year. Thus, they were more dependent on the entrepreneur’s

personal financial resources than new firms overall. In spite of this, however, their findings

revealed that technology-based firms raised larger amounts of capital than all firms during their

startup year. This was particularly true for growth oriented technology firms and technology

firms with high credit quality.

2.3.5 Capital Structure and Small and Medium Scale Enterprises

Examining the capital structure of firms and small and medium, Coleman (2005) examined

theories of capital structure pertaining to small firms and looked at the capital structure of small

to mid-sized manufacturing firms within the context of those theories and insisted that contrary

to the findings of prior research, these results revealed that industry sector was not a significant

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determinant of capital structure. These findings showed that capital structure in small to mid-

sized firms was determined by measures of firm size, firm age, organizational status,

profitability, and asset structure.

Jónsson (2011) inspired by findings of a paper by La Porta et al. (1996), compared the

determinants of capital structure of small and medium-sized firms between Europe and the

United States. Furthermore, the question raised was whether possible difference can stem from

the different legal origins of the two continents. Legal origins were divided into two parts, with

the United States of common-law origin and Europe of French civil-law origin. The determinants

consist of two parts. First, the firm-specific part, i.e. variables (growth, size, asset structure,

profitability and age) that had proved to work according to the trade-off theory and the pecking

order theory. Second, the country-specific part, i.e. legal rules (investor protection, legal rights,

contract enforcement and recovery rate) which were considered to be influenced by the legal

origins of a country. This study conducted by the use of panel data analysis, points to differences

in the determinants of capital structure between Europe and the United States. Results showed

that determinants act in the correct way as predicted by theory and earlier evidence. Again, the

results showed that U.S. SMEs employed higher proportion of long-term debt to total debt and

display less effect of asset structure on leverage than the European ones. This indicated that

European SMEs suffered from more problems of asymmetric information than the U.S. ones, and

these problems could be related to the fact that legal rules in Europe, of French civil-law origin,

were weaker than in the United States.

2.3.6 Capital Structure of Real Estate Firms

Lim, Zhao and Chai (2012) investigated the determinants of capital structure of real estate firms

in China. An empirical study on determinants of capital structure of real estate in Chinese listed

firms was conducted using a relatively regression of accounting data for 44 A-share financial

listed companies over the quarter from 2008 to the third quarter of 2011. First, they identified

that the pecking order theory in China was different from western countries. Second, the results

show that profitability, non-debt tax shields and liquidity were significant influence factors in

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financial sector, while others like size, growth, tangibility and non-circulating share should be

judged by the size of the company. Moreover, firm size and non-circulating shares were almost

positively related to the corporate leverage ratio. It was also found that Chinese institutional

characteristic affected the capital choice decision and the largely state ownerships did affect

capital structure choices.

Feng, Ghosh and Sirmans (1999) argued that much of the literature on capital structure excluded

Real Estate Investment Trusts (REITs) due mainly to the unique regulatory environment of these

firms. As such, the issue of how REITs choose among different financing options when they

raise external capital was largely unexplored. In this study, they examined the capital structure of

REITs to answer two questions: was there a relationship between market-to-book and leverage

ratios? and, did market-to-book have a temporary or a long-term impact on leverage ratios? Their

results suggested that REITs with high market-to-book ratios had high leverage ratios, and

historical market-to-book had long-term persistent impact on current leverage ratio. They

interpreted these findings as supportive of pecking order theory. When financing costs of adverse

selection exceeded costs of financial distress, pecking order was more relevant in explaining the

cross-sectional variation in capital structure.

2.3.7 Capital Structure and Textile Firms

Examining Capital Structure and textile firms Sheikh and Wang (2010) attempted to explore

those factors that influence the capital structure choice of textile firms in Pakistan and their

investigation was performed using panel data procedures for a sample of 75 firms listed on

Karachi Stock Exchange during 2002-2007. The results suggested that leverage was negatively

correlated with profitability, liquidity, and tangibility, and positively correlated with firm size

and growth opportunities. In particular, the negative relationships of profitability and liquidity,

and a positive relationship of growth opportunities with firm leverage confirmed the predictions

of pecking order hypothesis. A positive relationship of firm size with leverage confirmed the

predictions of trade-off theory. A negative relationship between tangibility and leverage was in

contradiction with trade-off theory; however it seemed to be consistent with the predictions of

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pecking order theory because of profound dependence of textile firms on short-term debt. Thus,

these findings suggested that some of the insights from modern finance theory were applicable to

Pakistan firms in that certain specific factors that were relevant for explaining the capital

structure in developed economies were also relevant to firms in Pakistan.

2.3.8 Capital Structure and Firm Ownership

Boodhoo (2009) was of the view that there had always been controversies among finance

scholars when it came to the subject of capital structure. So far, researchers had not yet reached a

consensus on the optimal capital structure of firms by simultaneously dealing with the agency

problem.

Examining capital structure and firm ownership Prasad, Green and Murinde (2008) critically

surveyed the key literature on corporate financing policy, capital structure and firm ownership in

order to identify the leading theoretical and empirical issues in this area. The theoretical

component of the survey attempted to reconcile competing theories of capital structure and

appraised recent models which use agency theory and asymmetric information to explore the

impact of managerial shareholdings, corporate strategy and taxation on the firm’s capital

structure. The empirical component focused on univariate analyses as well as multivariate

models of capital structure, and made a comparison between theoretical predictions and

empirical results. Implications were identified in terms of promising research ideas (PRIs) for

further research. The bulk of the empirical research that they surveyed was concerned with the

experience of a few western industrial countries, and the implications of this research were

assessed accordingly.

2.3.9 External Financing and Access to Finance

A survey of Literature suggests that external finance aid firms in accessing fund. Thus, Eric, Lam

and Wei (2009) offered a novel understanding of the cause of the external financing anomaly, a

well established observation that net overall external financing activities and future stock returns

were negatively related. They posited that recent studies argued that the external financing

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anomaly was driven by earnings management and/or investment growth. However, they found

that about half of the anomaly remains unexplained by these interpretations. The remaining

predictability is not due to exposures to conventional risks and firm characteristics, the accrual

factor, the asset growth factor, the wealth transfer hypothesis, or the issuer risk hypothesis, and is

not driven by performance de-listings or de-listings associated with negative returns or unknown

risks. Instead, it was attributed to the overvalued young and small unprofitable firms that lack

internal funds and had limited access to public debt markets relied heavily on equity and

modestly on private debt external financing to pursue their ambitious growth strategies through

heavily investing in research and development.

Park and Pincus (2000) argued that because of transactions costs and investor/manager

information asymmetries, internally generated funds should be less costly than funds raised by

issuing shares. They suggested that as firms use more internal funds relative to external equity,

their costs of equity capital would fall and the rate the market used to discount unexpected

earnings of such firms would be lower. They hypothesized that (1) firms having a higher

proportion of internal to external equity would have larger earnings response coefficients, and (2)

this effect would be magnified for high growth firms since the disparity between inside

information and publicly available information about high growth firms' investment

opportunities would be greatest and found that support for both hypotheses using pooled and

annual cross-sectional regressions after controlling for other determinants of ERCs. The results

were also generally robust to alternative measures of the mix of equity funding sources and of

unexpected earnings and to consideration of other factors affecting the mix of equity capital.

Gomes, Yaron and Zhang (2009) used a production-based asset pricing model to investigate

whether financing constraints were quantitatively important for the cross-section of returns.

Specifically, they used GMM to explore the stochastic Euler equation imposed on returns by

optimal investment. Their methods identified the impact of financial frictions on the stochastic

discount factor with cyclical variations in cost of external funds and found that financing

frictions provide a common factor that improved the pricing of cross-sectional returns.

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Moreover, the shadow cost of external funds exhibited strong procyclical variation, so that

financial frictions were more important in relatively good economic conditions.

Demirguc-Kunt and Maksimovic (2004) posited that in developing countries the growth of stock

markets affected corporate financing decisions thus stock market development tended to be

accompanied by higher corporate debt-equity ratios and more business for banks.

Cooper, Gulen and Schill (2008) tested the firm-level asset investment effects on returns by

examining the cross-sectional relation between firm asset growth and subsequent stock returns.

They posited that asset growth rates were strong predictors of future abnormal returns. They

compared asset growth rates with the previously documented determinants of the cross-section of

returns (i.e., book-to-market ratios, firm capitalization, lagged returns, accruals, and other growth

measures) and found out that a firm’s annual asset growth rate emerged as an economically and

statistically significant predictor of the cross-section of U.S. stock returns.

Hall (2002) focused on the financial market reasons for underinvestment in R&D that persisted

even in the absence of externality-induced underinvestment. He concluded that small and new

innovative firms experienced high costs of capital that was only partly mitigated by the presence

of venture capital; evidence for high costs of R&D capital for large firms was mixed, although

these firms did prefer internal funds for financing these investments; there were limits to venture

capital as a solution to the funding gap, especially in countries where public equity markets are

not highly developed; and further study of governmental seed capital and subsidy programs

using quasi-experimental methods was warranted.

Danielsen, Harrison, Ness and Warr (2010) examined the effect of audit fees on security market

transparency and in particular the case for firms that were frequent issuers of seasoned equity.

They found that equity issuers invest more heavily in audit services but benefit from greater

stock market liquidity as a result. Furthermore, they found that more liquid firms had lower ex

ante costs of capital and higher (less negative) SEO announcement returns. Their findings

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support the hypotheses that firms could improve their transparency by investing in audit services

and that such investment had real economic benefits.

Yattey (2006) examined the corporate financing pattern in Ghana particular. He investigates

whether Singh's theoretically anomalous findings that developing country firms made

considerably more use of external finance and new equity issues than developed country firms to

finance asset growth hold in the case of Ghana. Replicating Singh’s methodology, their results

showed that compared with corporations in advanced countries, the average listed Ghanaian firm

financed its growth of total assets mainly from short-term debt. The stock market, however, was

the most important source of long-term finance for listed Ghanaian firms. Overall, the evidence

in this paper suggested that the stock market was a surprisingly important source of finance for

funding corporate growth and that stock market development in Ghana had been important.

Nanda (2010) confirmed the finding that the propensity to start a new firm rises sharply among

those in the top five percentiles of personal wealth. This pattern according to him was more

pronounced for entrants in less capital intensive sectors and posited that prior to entry, founders

in this group earn about 6% less compared to those who stayed in paid employment as their firms

were more likely to fail early and conditional on survival, less likely to make money. He asserted

that this pattern was only true for the most-wealthy individuals, and was attenuated for wealthy

individuals starting firms in capital intensive industries. Taken together, these findings suggested

that the spike in entry at the top end of the wealth distribution were driven by low-ability

individuals who could afford to start (and sometimes continue running) weaker firms because

they did not face the discipline of external finance

Asif, Rasool and Kamal (2011) examined the relationship between dividend policy and financial

leverage of 403 companies listed with Karachi Stock Exchange during the period 2002 to 2008.

They argued that dividend policy, vastly followed by the companies, was tested by applying the

extended model of Linter (1956) with the debt ratio of the firm, the previous year’s dividend

yield as its independent variables and change in earnings as a dummy variable. Descriptive

statistics for their entire variables were calculated and then correlation matrix was calculated to

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identify the preliminary relationship among all the variables, followed by regression analysis on

panel data to examine the significance and magnitude through fixed and random effects models.

Theoretical assertions were justified through random effect model that the level of corporate debt

(leverage) and widely practiced dividend policy, significantly, affected the dividend policy of the

Pakistani firms. On the other hand, financial leverage was found to have a negative impact on

dividend payout, indicating less dividend payments by high-debt firms. Their findings also

confirmed that change in earnings had no significant impact on dividend policy in case of

Pakistani firms while the dividend yield had positive impact and vice versa. Fixed effect model,

applied for the study, supported only the significant effect of dividend yield on dividend per

share.

Lin and Wang (2011) hold the opinion that earnings information was a performance evaluation

of the managers. It was also a form of communication for stakeholder and market efficiency was

an important factor which affects earnings information. They said the more efficient the market

was, the less attention stakeholder would pay to earnings information, and vice versa. They

asserted that corporate financing policy means an increase in external monitoring could affect

corporate idiosyncratic risk thus they examined the relationship between the value relevance of

accounting information, financing policy and idiosyncratic risk based on the data from Taiwan

and the U.S. capital market. Their findings showed that, in Taiwan market, the information value

of earnings had a positive impact on corporate debt financing, and that cash flows had a positive

impact on corporate equity financing activities. However, according to them in the U.S., since

capital market was more efficient, earnings information had a weaker impact on corporate

financing policy. Value relevance of cash flows was negatively related to idiosyncratic risk in

Taiwan. Equity financing activity would significantly increase idiosyncratic volatility and

subsequently decreases it under the consideration of earnings’ value relevance (incremental

information). On the other hand, in the US, equity financial activity decreased idiosyncratic

volatility and subsequently increased it considering earnings’ value relevance (incremental

information). If incremental information of cash flows was taken into consideration, equity

financial activity would then decrease idiosyncratic volatility after increasing it.

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Lyandres (2007) examined the effects of costly external financing on the optimal timing of a

firm's investment by altering the optimal investment timing and the sensitivity of investment to

internal cash flow. He said importantly, the relation between the cost of external funds and

investment–cash flow sensitivity was non-monotonic. Investment–cash flow sensitivity was

decreasing in the cost of external financing when it was relatively low and increasing in the

financing cost when high. Empirical tests examining investment–cash flow sensitivities within

groups of firms classified by proxies for their costs of external funds provided evidence

consistent with the model.

Eberhart, Altman and Aggarwal (1998) assessed the stock return performance of 131 firms using

differing estimates of expected returns and consistently found evidence of large, positive excess

returns in 200 days of returns following emergence. They also examined the reaction of their

sample firms’ equity returns to their earnings announcements after emergence. The positive and

significant reactions suggested that their results were driven by the market’s expectation errors,

not mis-measurement of risk. Their results provided an interesting contrast, but not a

contradiction, to previous work that had documented poor operating performance for firms.

Chaya and Suhb (2008) used firm-level data from thirty-five countries over the period 1998-

2004. They conducted a comprehensive investigation of the relation between financial

constraints and the sensitivity of investments to internal and external funds. Their investigation

showed that, in the majority of countries, the investments of financially constrained firms were

not highly sensitive to internal funds, which confirmed the results of prior U.S. studies.

Moreover, in many countries, financially constrained firms used substantial amounts of external

funds, and their investments tended to be more sensitive to external financing than to internal

financing. Their evidence is at odds with the standard view in the financial constraint literature

that financially constrained firms faced restricted access to external financing.

Hennessy and Whited (2007) applied simulated method of moments to a dynamic model to infer

the magnitude of financing costs. The model featured endogenous investment, distributions,

leverage, and default. The corporation faced taxation, costly bankruptcy, and linear-quadratic

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equity flotation costs. For large (small) firms, estimated marginal equity flotation costs start at

5.0% (10.7%) and bankruptcy costs equal to 8.4% (15.1%) of capital. Estimated financing

frictions were higher for low-dividend firms and those identified as constrained by the Cleary

and Whited-Wu indexes. In simulated data, many common proxies for financing constraints

actually decreased when they increase financing cost parameters.

Inderst and Miller (2003) studied optimal financial contracting for centralized and decentralized

firms. Under centralized contracting, headquarters raised funds on behalf of multiple projects.

Under decentralized contracting, each project raised funds separately on the external capital

market. The benefit of centralization was that headquarters could use excess liquidity from high

cash flow projects to buy continuation rights for low cash flow projects. The cost was that

headquarters might pool cash flows from several projects and self finance follow-up investments

without having to return to the capital market. Absent from any capital market discipline, it was

more difficult to force headquarters to make repayments, which tightens financing constraints ex

ante. Cross-sectionally, their model implied that conglomerates should have a lower average

productivity than stand-alone firms.

Jansson (2000) was of the view that a dynamic process underlying firms’ discrete financial

choices had previously been found, but without controlling for unobserved heterogeneity, this

dependence could either be of a ”true” nature or an effect of firm-specific characteristics that we

could not observe. Jansson (2000) study extended previous research focusing on firms’ discrete

external financing decision by adapting a model by Honoré and Kyriazidou (2000), which

accommodated both fixed effects and a lagged dependent variable, which makes it possible to

establish the nature of the dependence. They found that there was a smoothing of financing, even

after controlling for unobserved heterogeneity, and also that unobserved heterogeneity played a

significant explanatory role.

Faria et al (2006) traced the history of where and why investors from the most advanced

countries directed funds, ultimately helping finance economic development in emerging market

countries. They analyzed the determinants of international investors’ willingness to hold the

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external liabilities issued by emerging market countries, through cross-country regressions for

both prices (bond spreads) and quantities (bond market capitalization or stocks of external

liabilities) estimated at various points during two waves of financial globalization (1870–1913

and 1914-2006). The data were drawn from primary sources for the historical period, and the

much-expanded, new vintage of the Lane and Milesi-Ferretti (2006) data set for the modern

period. The results suggested that, throughout the past one and a half centuries, a combination of

human capital (including informal human capital) and institutional quality had been a key

determinant of emerging market countries’ ability to attract international investors.

Using a firm-level survey database covering 48 countries, Demirgüç-Kunt and Maksimovic

(2004) investigated how financial and institutional development affected financing of large and

small firms. Their database was not limited to large firms, but included small and medium firms

and data on a broad spectrum of financing sources, including leasing, supplier, development and

informal finance. Small firms and firms in countries with poor institutions use less external

finance, especially bank finance. Protection of property rights increased external financing of

small firms significantly more than of large firms, mainly due to its effect on bank and equity

finance. Small firms did not use disproportionately more leasing or trade finance compared to

larger firms. Financing from these sources was positively associated with the financial

development and did not compensate for lower access to bank financing of small firms in

countries with underdeveloped institutions.

Nofsinger and Wang (2009) were of the view that the typical new start-up firm acquires external

financing in stages through its development. The later stages of financing (venture capital and

initial public offerings) had been frequently examined. The early stages of financing (initial

capitalization and angel investing) had rarely been analyzed. Nofsinger and Wang (2009)

examined the determinants of the initial start-up financing of entrepreneurial firms in 27

countries. There are information asymmetries and moral hazard problems inherent in the funding

of a start-up firm. Institutional investors seemed to rely on their abilities to reduce the

information asymmetry and the quality of investor protection to reduce the moral hazard. On the

other hand, informal investors were also common in initial start-up funding. They tended to use

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the type of products and entrepreneurial experience in the new firm as a signal of quality to

reduce information asymmetries. They also seemed to rely more on their connectedness to the

entrepreneur through a personal relationship to reduce the moral hazard problem.

Artmann, Finter, and Kempf (2011) conducted a comprehensive asset pricing study based on a

unique dataset for the German stock market. For the period 1963 to 2006 and showed that value

characteristics and momentum explained the cross-section of stock returns. Corresponding factor

portfolios had significant premiums across various double-sorted characteristic-based test assets.

In a horse race of competing asset pricing models the Fama-French 3-factor model did a poor job

in explaining average stock returns. The Carhart 4-factor model performed much better, but a 4-

factor model containing an earnings-to-price factor instead of a size factor did even slightly

better.

2.3.10 Determinant of Stock Returns

Olowoniyi and Ojenike (2011) opined that identifying the factors that influence stock returns

was a major concern for practice and academic research. They thus investigated the determinants

of stock returns of listed firms in Nigeria using panel econometric approach to analyse panel data

obtained from 70 listed for the period 2000-2009. The fixed effect (FE), random effect (RE) and

Hausman-test based on the difference between fixed and random effects estimators were

conducted. Their findings suggested that expected growth and size positively influenced stock

return while tangibility negatively impacted on stock return of listed firms. Efforts at improving

size of the firms and adjustment of firms’ tangibility to a positive side was suggested to improve

financial situation of firms through stock return.

Elsas, Flannery and Garfinkel (2006) assembled a sample of 1,558 large investments made by

1,185 firms over the period 1989-1999, and raised two main issues, firstly how did firms pay for

these large investments? and how did the stock market subsequently evaluate them? They found

that major investments were mostly externally financed. The pecking order and market timing

effects on capital structure were transitory. Firms moved toward target leverage ratios. Long-run

abnormal stock returns were not generally consistent with the hypothesis that managers tend to

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overinvest with internal funds and they again argued that only firms financing large projects with

(newly-raised) external funds exhibited reliably negative abnormal returns over the subsequent 1

– 3 years.

Creamer et al (2009) reviewed financial trends in manufacturing and in mining over the past

half-century were reviewed and drew attention on two aspects of the financial growth of these

industries. They examined the long-run tendencies in internal and external financing and

compared the various debt and equity components of external financing and the trends in total

debt and total equity (both from internal and external sources).

Perez-Quiros and Timmermann (1999) was of the opinion that recent imperfect capital market

theories predicted the presence of asymmetries in the variation of small and large firms' risk over

the economic cycle as such small firms with little collateral should be more strongly affected by

tighter credit market conditions in a recession state than large, better collateralized ones. They

adopted a flexible econometric model to analyse these implications empirically. According to

them consistent with theory, small firms displayed the highest degree of asymmetry in their risk

across recession and expansion state and this translated into a higher sensitivity of these firms'

expected stock returns with respect to variables that measured credit market conditions.

Wang et al (2010) studied and compared the determinants of stock returns in the 1987 and 2008

stock market meltdowns with the multivariate regression analysis technique. They found out that

technical insolvency risk and bankruptcy risk were significant determinants of stock returns in

the 2008 market meltdown. They said investors were also somewhat concerned with bankruptcy

risk in the 1987 market meltdown. However, technical insolvency risk was not a significant

determinant of stock returns in the 1987 meltdown. Their findings indicated that stocks with

higher betas, larger market cap, and greater return volatility lost more value in both meltdowns.

They found out that the market-to-book ratio to be a significant determinant of stock returns in

the 2008 meltdown but not in the 1987 meltdown. Their study also stated that stock illiquidity to

be a significant determinant of stock returns in the 1987 meltdown but not in the 2008 meltdown.

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With data for two most important stock market meltdowns in U.S. history since the Great

Depression.

Haque and Suleman (2012) explored the association among macro-determinants and stock

returns by analyzing the reaction of macroeconomic variables on individual equity returns. For

this purpose a panel data of 394 listed companies listed in the Karachi Sock Exchanger over the

period of 1998-2009 was used for empirical analysis. The results revealed that volatility and

gross domestic product had a significant positive effect on individual equity return, while,

inflation, interest rate, money supply and budget deficit confirmed a significant negative

association. The findings also highlighted a significant positive effect of exchange rate on equity

return of textile sector. To recapitulate, returns of different sectors reacted differently to the same

macro variable.

While assuming that failure risk is the sole determinant of risk premiums, Fitzpatrick and Ogden

(2009) developed and tested a hypothesis that the following six asset pricing anomalies share a

common link via a mispricing relationship involving operating profit and external financing: (1)

The raw profitability anomaly; (2) The failure-risk anomaly; (3) post-earnings announcement

drift; (4) The external financing anomaly; (5) The book-to-market anomaly; and (6) The accruals

anomaly. Using average cross-sectional data on 314 portfolios U.S. firms (1980-2007) that were

developed by sorting and cross-sorting on risk-proxy, cash flow, and past return variables, they

found a common link among the first five anomalies, while evidence related to accruals was

mixed. There were also able to find a general positive relationship between failure risk and future

short- and long-term returns, but only after adjusting for this 'common link' source of mispricing.

Stock price 'hyping' in advance of external financing issues was a plausible partial explanation

for common link mispricing.

Baker and Wurgler (2003) examined how investors’ sentiment affected the cross-section of stock

returns. They said theory predicted that a broad wave of sentiment would disproportionately

affect stocks whose valuations were highly subjective and difficult to arbitrage. They tested this

prediction by studying how the cross-section of subsequent stock returns varied with proxies for

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beginning-of-period investor sentiment. When sentiment was low, subsequent returns were

relatively high on smaller stocks, high volatility stocks, unprofitable stocks, non-dividend-paying

stocks, extreme-growth stocks, and distressed stocks, consistent with an initial underpricing of

these stocks. When sentiment was high, on the other hand, these patterns attenuated or fully

reversed. The results were consistent with theoretical predictions and unlikely to reflect an

alternative explanation based on compensation for systematic risks.

Yartey (2006) examined the corporate financing pattern in Ghana. In particular, it investigated

whether Singh's theoretically anomalous findings that developing country firms made

considerably more use of external finance and new equity issues than developed country firms to

finance asset growth hold in the case of Ghana. Replicating Singh’s methodology, the results

showed that compared with corporations in advanced countries, the average listed Ghanaian firm

finances its growth of total assets mainly from short-term debt. The stock market, however, was

the most important source of long-term finance for listed Ghanaian firms. Overall, the evidence

suggested that the stock market was a surprisingly important source of finance for funding

corporate growth and that stock market development in Ghana had been important.

Livdan, Sapriza and Zhang (2006) were of the view that the more financially constrained firms

were riskier and earned higher expected returns than less financially constrained firms, although

this effect could be subsumed by size and book-to-market. Further, because the stochastic

discount factor made capital investment more procyclical, financial constraints were more

binding in economic booms. These insights arose from two dynamic models. In Model 1, firms

faced dividend nonnegativity constraints without any access to external funds. In Model 2, firms

could retain earnings, raised debt and equity, but faced collateral constraints on debt capacity.

Despite their diverse structures, the two models share largely similar predictions.

2.4 Review Summary

The theoretical and empirical literature surveyed above shows the extent of the impact of

external financing on performance of firms. While some studies reveal that external financing

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has positive impact on firm performance, others reveal that its impact on firm performance is

negative and non-significant.

The opinion and findings of Asif, Rasol and Kamel (2011), Khan (2010), Olowoniyi and Ojenike

(2011), Elias, Flannery and Gerfinkel (2006), Linter (1962) suggest the some model proxies

agreed that they have impact on firm performance. For instance, Asif, Rasol and Kamel (2011)

were of the opinion that dividend policy vastly followed by firms has negative impact on

dividend payout indicating less dividend payment by high-debt firms. Also, Khan (2010) shows

that financial leverage measured by short term debt to total assets and total debt to total assets

has a significant and negative relationship with firm performance measured by return on assets.

Again Olowoniyi and Ojenike (2011), Elias, Flannery and Garfinkel (2006) showed that external

financing positively enhances growth and size however, it negatively impact on stock return of

listed firms.

The opinion and findings from the above show that there is no consensus reached on the impact

of external financing on firm performance. This lack of consensus could be attributed to so many

reasons. This includes the perception of investors on external financing in developing economies

where most investors view the use of external finance as a symptom of poor performance of

firms. Also, the inability of most firms to access fund from financial institutions also inhibited

firms’ ability to raise funds from external sources thereby limiting growth potentials.

Thus, taking cognizance of this lack of consensus and seeking to overcome some of the short

comings as well as limitations noticed in the studies reviewed with particular emphasis on Abor

(2008), this study fills this important knowledge gap by modifying Abor (2008) through the

introduction of a panel data set in determining the impact of external financing (Debt

component) on performance of manufacturing firms in Nigeria and also including control

variables such as firm size and assets structure in line with the works of Abor (2008) for the

period 1999 to 2012.

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CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Research Design

According to Onwumere (2005), a research design is a kind of blueprint that guides the

researcher in his or her investigation and analyses. The research design adopted for this research

is the ex-post facto research design and the adoption of this research design hinged on two

reasons. Firstly, the study relied on historic accounting data obtained from the financial

statements and accounts of quoted manufacturing firms in the Nigeria Stock Exchange, from

1999 – 2012, as such the event under investigation had already taken place and the researcher

does not intend to control or manipulate the independent variables. The inability of the

researcher to manipulate these variables is a basic feature of ex-post facto research design.

Secondly, as described by Kerlinger (1970), the ex-post facto research design also called causal

comparative research is used when the researcher intends to determine cause-effect relationship

between the independent and dependent variables with a view to establishing a causal link

between them. This also led to the adoption of this research design in this study.

3.2 Sources of Data

The issue of data is at the very centre of research and also the nature of data for any study

depends entirely on the objectives of the research and the type of research undertaken

(Onwumere, 2005). Therefore, consistent with the above and also in line with researches

conducted in this area of finance where most data utilized were obtained from the financial

statements and accounts of sampled firms (Ezeoha, 2007), the nature of data for this research is

secondary nature. Secondary data are data which have been processed, collated and exist in

published form (Onwumere, 2005). The secondary data sources used in this study was extracted

from the published financial statements and accounts of quoted manufacturing firms for the

period 1999 – 2012. Company annual statements and reports are deemed to be reliable because

they are statutorily required to be audited by a recognized auditing firm before publication

(CAMA, Section 331 – 335).

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3.3 Population and Sample Size

The population of this study comprised selected quoted manufacturing firms on the Nigerian

Stock Exchange. These are the Agriculture; Airline; Automobile; Breweries; Building materials;

Chemical and Paints; Commercial Services; Computer and Office Equipments; Conglomerates;

Construction; Engineering Technology; Footwares; Food, Beverages and Tobacco; Health Care;

Hotel and Tourism; Industrial and Cosmetic Products; Information and Communication

Technology; Leasing; Machinery and Marketing; Maritime; Media; Packaging; Petroleum;

Printing and Publishing; Road Construction; Road Transportation and Textiles for the period

1999 to 2012.

3.4 Operational Definition of Model Variables

3.4.1 Independent Variable

External Finance

This variable measures the proportion of permanent capital in the financing mix of a firm.

Essentially, this is based on the postulation that in the Nigerian financial system, the market is

skewed towards equity financing. Thus, the best measure of external financing in the Nigerian

corporate environment is external equity. In line with the works of Abor (2008), this study

measures external finance by taking the natural logarithm of total debt (long and short term) of

manufacturing firms in Nigeria in line with the works of Abor (2008). Hence, it was represented

as:

External Financing = Log of total debt…………………………………………………(i) 3.4.2 Dependent Variables

Earnings per share

Earnings per share can be described as the book value reward of an investor for making his

investment. According to Hyderabad (1997) the bottom line of income statement is that it is an

indicators of performance of think tank or top level of the company, therefore, ordinary investors

lacking in-depth knowledge and inside information mainly based their decision on earnings per

share to make their investment decision, so it should be the objective of the firm to maximize the

EPS from the point of view investors. In this study, EPS was measured by profit after tax divided

by total number of shares outstanding. Thus,

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EPS = Profit after tax/ No of Ordinary shares outstanding…………………………….. (ii)

Payout Ratio

Plowing earnings back into new investments may result in growth in earnings and dividend but it

does not add to the current stock price if that money is expected to earn only the return that

investors require (Brealey, Myers and Marcus, 2004). Thus plowing earnings back does not add

to value if investors believe that the reinvested earnings will earn a higher rate of return. Hence,

in this study, the payment ratio is adopted as a dependent variable to measure the perception of

investors on Nigeria firm’s stock returns. In this study, payout ratio will be measured by total

equity dividend divided by profit after tax. Hence, it will be represented as;

Payout Ratio = Dividend per Share/ Earnings per Share……………………………………….(iii) Dividend per share

According to Bhaduris (2002) dividends payment acts a signal of financial health of firms to

outsiders. They payment of dividend decrease the amount of internal funds and increase the need

for external financing. As such the dividend policies of firms allows them release resources when

a firm has no profitable projects and conveys information about a firm’s future expectations to

capital markets, thus it is very important in measuring value of firms. According to Frank and

Goyal (2004) there is a positive relationship between payout ratio and debt, thus accordingly we

expect a positive relationship between external finance and dividend per share of Nigerian firms.

DPS is the total dividend paid out over an entire year divided by the number of outstanding

ordinary shares issued (Pandey, 2005). The proxy used in this research to represent DPS as

adopted from Pandey (2005) is;

DPS = Dividend Paid/ No of Ordinary shares outstanding…………………… (iv)

Return on Assets

This is a profitability measure that evaluates the performance of the firm by dividing the profit

before interest taxes and depreciation by the total assets. According to Abor (2008) a high ROA

means the investment gained compare favourably to the cost investment. As a performance

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measure, ROA is used to evaluate the efficiency of an investment or to compute the efficiency of

number of different investment.

Return on Equity

Return on equity is the ratio of the net income of business during a given year to its stockholder’s

equity during that year. It is a measure of profitability of stockholders investment. It shows net

income as percentage of shareholders; equity. While net income is the after tax income and

average shareholders’ equity will be calculated by dividing the sum of shareholder equity at the

beginning and at the end of the year by 2. This will be in line with the works of Abor (2008).

3.4.3 Control Variables

Assets structure

Assets structure is an important determinant of the capital decision. According to Harris and

Raviv (1991) the firm’s assets are tangible and have a greater liquidation value. In this study the

asset structure of Nigerian firms will be measure by fixed assets divided total asset in line with

the works of Abor (2008). As asserted by Abor (2008) the more tangible assets are, the more

collateral would be. This was predicted by the pecking order theory which assumes that firms

holding more tangible assets will be less prone to asymmetric information problems and reduce

the agency cost.

Asset Structure = Fixed Assets/Total Assets ……………………………………….. (vii)

Firm Size

According to Booth et al (2001), size plays an important role in capital structure and Hussain and

Matlay (2007) assert that firms strive for external sources of finance only if the internal sources

are exhaust. In this study, size will be measured by taking the natural log of total asset.

Therefore, in this study we consider size of a firm to be an important control variable.

Firm Size = Log of total assets ……………………………….. …………………………… (viii)

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3.5 Model Specification

This study adopted Abor’s (2008) study to examine the impact of external finance on stock

returns of Nigerian firms. According to Abor (2008) panel data can control for individual

heterogeneity due to hidden factors, which, if neglected in time-series or cross-section

estimations leads to biased results (Baltagi, 1995). The panel regression equation differs from a

regular time-series or cross-section regression by the double subscript attached to each variable.

Therefore, the general form of the model for this study is specified as:

Υit = α + βΧit + µit…………………………………………………..................................... (ix) with the subscript i denoting the cross-sectional dimension and t representing the time series

dimension. The left-hand variable, Yit, represents the dependent variable in the model, which is

the firm’s debt ratio. Xit contains the set of explanatory variables in the estimation model, α is

the constant and β represents the coefficients while µ represents the error term.

However, in line with the hypotheses stated in this study, the model was specified as follows. For

hypothesis one which states that External financing do not have positive and significant impact

on earnings per share of Nigerian manufacturing firms,, it was represented as;

EPS = a + β1EF + β2AS + β3FS+µ………………………………………………… (x) where; EPS = Earnings per share EF = External finance AS = Asset structure FS = Firm size

For hypothesis two which states that external financing do not have positive and significant

impact on payout ratio of Nigerian manufacturing firms,, it was represented as:

POR = a + β1EF + β2AS + β3FS + µ……………………………………………….(xi) where; POR = Payout ratio EF = External finance AS = Asset structure FS = Firm size

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For hypothesis three which states that external financing do not have positive and significant

impact on returns on dividend per share of Nigerian manufacturing firms,, it was represented as;

DPS = a + β1EF + β2AS + β3FS +µ………………………………………………. (xii) where; DPS = Dividend per share EF = External finance AS = Asset structure FS = Firm size

Hypothesis four which states that external financing does not have positive and significant

impact on returns on return on Assets of Nigerian manufacturing firms, it was represented as;

ROA = a + β1EF + β2AS + β3FS +µ………………… …………………………….(xiii) where;

ROE = Return on Assets EF = External finance AS = Asset structure FS = Firm size

Lastly for hypothesis five which states that external financing do not have positive and

significant impact on returns on equity of Nigerian manufacturing firms,.

ROE = a + β1EF + β2AS + β3FS + µ……………………………………………….(xiv) where;

ROE = Return on Equity EF = External finance AS = Asset structure FS = Firm size

3.6 Techniques of Analysis

The hypotheses stated were tested using the Ordinary Least Square model. The signs and

significance of the regression coefficients was relied upon in explaining the nature and influence

of the independent and dependent variables as to determine both magnitude and direction of

impact. Regression analysis is often concerned with the study of the dependence of one variable,

the dependent variable, on one or more other variables, the explanatory variables, with a view to

90

estimating and/or predicting the population mean or average value of the former in terms of the

known or fixed (in repeated sampling) values of the latter (Gujarati and Porter, 2009).

Most commonly, regression analysis estimates the conditional expectation of the dependent

variable given the independent variables that is, the average value of the dependent variable

when the independent variables are held fixed. Less commonly, the focus is on a quartile, or

other location parameter of the conditional distribution of the dependent variable given the

independent variables. In all cases, the estimation target is a function of the independent

variables called the regression function. In regression analysis, it is also of interest to

characterize the variation of the dependent variable around the regression function, which can be

described by a probability (see, Gujarati, 1995).

91

REFERENCES

Abor, J. (2008). Determinants of the capital structure of Ghanaian firms. Journal of Financial Economics, 43(5)782-798 Baltagi, B.H. (1995). Econometric Analysis of Panel Data. Chichester: Wiley Booth, L., V. Aivazian, A. Demirguc-Kunt & Maksimovic, V (2001). Capital structures in developing countries. Journal of Finance, 55(1): 87–130 Beasley R.A., Myers S.C & Marcus, A.J (2007). Fundamentals of corporate finance. 5th ed. Boston: McGraw-Hill/Irwin. CAMA (1990). The Federal Government of Nigeria, 1990 Douglas, A.L, W.G William and R.D Mason (2002). Statistical Techniques in Business and Economics. Boston; McGraw-Hill Irwin Ezeoha, A.K. (2007). The impact of major firm characteristics in the financial leverage of quoted companies in Nigeria. A PhD thesis Presented to the Department of Banking and Finance, University of Nigeria, Enugu Campus Frank, M. Z. & Goyal, V.K (2004). Testing the pecking order theory of capital structure. Journal of Financial Economics, 67(4)456-478 Gujarati, D.N. and Porter D.C (2009). Basic econometrics fifth edition. Singapore: Mcgraw- Hill International Edition Gujarati D.N (1995). Econometrics. Singapore: Mcgraw- Hill International Edition Harris, M & Raviv, A (1990). Corporate control contests and capital structure; an empirical test. Journal of Managerial and Decision Economics, 15(7)89-112 Hyderabad, R.L. (1997). EPS management; an analysis. The Management Accountant, Volume 35(9)4-35 Kerlinger, F.N. (1973), Foundations of behavioural research techniques in business and economics Eleventh Edition. Boston: McGraw Hill Irwin Myers, S. (1984). The capital structure puzzle. Journal of Finance. 39(8)575–97. Onwumere, J.U.J (2005). Business and economic research method. Lagos: Don-Vinton Limited Pandey, I M (2005). Financial management. Nineth Edition, New Delhi Oikes Publishing House PVT Ltd

92

Smith, C W & Watts, R.L. (1982). Incentive and tax effect on executive compensation plans. Australian Journal of Management, 7(10)253-279 Yartey, C.A. (2006). The stock market and the financing of corporate growth in Africa: the case of Ghana. Journal of Financial Intermediation. 56(12)1205-1234.

93

CHAPTER FOUR

PRESENTATION AND ANALYSIS OF DATA

4.1 Presentation of Data

Data are presented and interpreted in line with the objectives of the study. The abridged

annualized ratio values used to test the hypotheses are presented in tables 4.1. The rest of the

data is presented as appendix 1.

Table 4.1 Model Proxies

OBS COMPANY NAME EXF EPS DPS POR ROA ROE LogAS SZ

1 UAC PLC 0.16 1.99 1.1 1.81 0.1 0.12 7.83 6.5

15 ARBICO 0.16 0.73 0 0.09 0.11 6.09 4.71

30 CAPPA& D’ALBERTO PLC 0.16 2.57 0 0.64 0.76 6.06 5.7

45 COSTAIN 5.02 0.03 0 0.4 0.16 5.13 4.52

60 G. CAPPA 0.16 0.41 0 0.01 0.01 6.94 4.71

75 ROADS NIGERIA 0.16 3.66 0.5 7.32 0.1 0.12 6.07 4.86

91 UACN PROPERTY 0.16 1.69 0.55 3.07 0.05 0.06 7.69 6.36

104 DN TYRE & RUBBER PLC 0.16 0.09 0 0.15 0.18 6.79 5.59

109 CHAMPIONS BREWERIES 0.16 1.37 0 0.32 0.38 6.43 6.09

119 GOLDEN BREWERIES 0.16 2.27 0 0.19 0.23 6.47 5.66

134 A.G LEVENTIS 0.16 0.29 0 0.06 0.07 7.1 5.81

148 CHELLARAMS PLC 0.16 0.3 0.1 3 0.08 0.09 6.63 5.34

162 JOHN HOLTS 0.16 2.63 0 0 0 7.01 4

176 SCOA NIGERIA PLC 0.16 0.33 0.08 4.13 0.22 0.26 6 5.33

190 TRANSCORP 0.32 0.21 0 0.18 0.26 7.6 6.73

195 DANGOTE CEMENT 0.45 0.07 0 0.3 0.36 8.52 8.03

200 DN MEYER PLC. 0.4 0.73 0 0.09 0.14 6.39 5.37

214 FIRST ALUMINUM NIGERIA PLC 0.18 1.59 0 0.04 0.04 6.91 5.52

228 IPWA PLC 0.14 11.61 0 0.33 0.39 5.37 4.78

243 LAFARGE CEMENT WAPCO NIGERIA PLC

0.31 1.63 0 0.06 0.09 8.12 7.69

248 PAINTS & COATING MANUFACTURES NIGERIA PLC

3.11 0.13 0 0.54 -0.26 5.3 5.03

253 VITAFOAM 1.67 0.63 0.3 2.1 0.37 -0.89 6.34 5.71

267 VONO PRODUCT 0.3 1.32 0 0.18 0.25 6.35 5.6

281 PZ CUSSONS 0.17 1.67 0.86 1.94 0.25 0.3 7.51 6.72

295 UNILEVER 0.22 1.11 0 0.4 0.41 7.18 6.62

309 EKOCORP PLC 0.17 0.06 0 0.03 0.03 9.19 7.47

325 UNION DIAGONISTIC AND CLINICAL SERVICES

0.13 0.04 0 0.11 0.12 6.29 5.2

94

OBS COMPANY NAME EXF EPS DPS POR ROA ROE LogAS SZ

330 EVANS MEDICAL 1.98 0.13 0 0.03 -0.18 6.32 3.94

345 MORRISON INDUSTRIES 0.26 0.22 0 0.07 0.1 5.68 4.52

359 FIDSON HEALTH CARE 0.23 0.31 0.1 3.1 0.17 0.22 6.45 5.67

364 PHARMA DECO 2.31 4.66 0 0.62 -1.71 5.87 5.67

380 ASHAKA CEMENT 0.08 1.51 0.3 5.03 0.18 0.2 7.39 6.6

394 AFRICAN PAINT 0.02 0 0 0.03 0.03 5.66 4.16

399 BERGER PAINTS 1.33 2.03 0 0.38 -1.16 6.14 5.65

413 CHEMICAL AND ALLIED 0.05 3.15 3 1.05 3.53 3.73 5.51 5.95

427 CEMENT COMPANY OF NORTHERN NIGERIA

0.26 1.01 0 0 0 8.57 8.03

441 UTC NIGERIA 0.17 0.06 0 0 0 6.42 4.9

455 UNION DICON SALT 0.05 0 0 0.15 0.16 6.18 5.34

470 CADBURY NIGERIA 15.52 0.38 0 1.97 -0.17 6 6.07

484 NESTLE NIGERIA 0.88 19.08 0 0.35 0.56 7.72 7.1

498 NIGERIA ENAMELWARE 0.1 1.1 0 2.12 2.36 4.72 4.87

517 BETA GLASS COMPANY 0.09 2.95 0 0.16 0.18 7.06 6.17

526 FLOUR MILL 0.45 9.67 2 4.84 0.32 0.35 7.9 7.23

543 NATIONAL SALT COMPANY 0.88 0.62 0 0.62 5.25 6.52 6.22

552 P.S. MANDRIDES PLC 0.03 0 0 0.24 0.25 5.11 4.46

558 GUINNESS 0.08 19.31 7.5 2.57 0.4 0.44 7.7 7.12

567 INTERNATIONAL BREWERIES 0.08 0.09 0 0.02 0.02 6.94 5.3

576 NIGERIA BREWERIES 0.05 4.01 3.54 1.13 0.47 0.49 7.98 #VALUE!

585 7UP 0.41 3.43 1.5 2.29 0.1 0.11 7.43 6.36

595 DANGOTE FLOUR MILL 0.07 0.54 0 0.18 0.2 7.73 6.73

Source: Nigerian Stock Exchange Factbook (Various Years) Note: Obs = Observation, EXF = External Finance, EPS = Earnings per share, DPS = Dividend per share, POR = pay-out ratio, ROA = Return on Asset, ROE = return on Equity, LogAS = Logarithm of Asset Structure, SZ = Size of Firm

Table 4.2 presents the descriptive statistics which is used to explain the movement of the model

proxies in line with the objectives of this study.

Table 4.2: Descriptive Statistics EXF EPS DPS POR ROA ROE LOGAS SZ

Mean 0.8425 6.3781 3.748370 4.5647 0.5571 0.4148 6.3067 5.5348 Median 0.1600 1.6850 0.685000 1.9400 0.2750 0.2600 6.2800 5.5550 Maximum 52.0100 70.0100 40.00000 136.50 17.220 21.030 9.1800 7.8600 Minimum 0.0000 0.0100 0.010000 0.0100 0.0000 -9.4200 3.8000 1.6000 Std. Dev. 4.2529 13.086 8.248078 10.756 1.3264 1.7547 1.0178 1.0095 Skewness 10.3723 3.1493 3.009034 8.211 9.0121 5.968 0.0768 -0.2269 Kurtosis 115.379 12.732 11.49171 90.245 103.16 78.699 2.7068 3.2882 Jarque- 146918. 1511.95 1218.670 88666.79 116505.9 66069.76 1.2325 3.2529

95

Bera Probability 0.0000 0.0000 0.000000 0.0000 0.0000 0.0000 0.5399 0.1966

Source: Researcher’s E-view Result

(a) Objective One: To examine the impact of external financing on the earnings per share of the quoted manufacturing firms in Nigeria

Table 4.1 and 4.2 present the abridged data and descriptive statistics for the period 1999 to 2012.

As revealed from the tables, it showed that the mean of the external finance of quoted Nigerian

manufacturing firms was 0.8425 while the median was 0.1600. As revealed by the skewness,

there was a positive skewness (10.37) of external finance indicating that the degree of departure

from the mean of the distribution is positive revealing that overall there was a consistent increase

in external finance from 1999 to 2012. Though as indicated by the Kurtosis which was 115.37 >

3 which is the normal value indicates that the degree of peakedness within the period of this

study were not normally distributed as most of the values did not hover around the mean. The

Jarque-Bera statistic is an indication of the normality of distributions was 146918 and since the

probability was equal to zero, the distribution was not normally distributed.

From the table also, the average earnings per share are 6.378 while the median was 1.685. The

maximum earnings per share were N70.00 while the least was N0.01k. The standard deviation

was 13.086. As revealed by the skewness, there was a positive skewness (3.149) of earnings per

share indicating that the degree of departure from the mean of the distribution is positive

revealing that overall there was a consistent increase in earnings per share from 1999 to 2012. As

indicated by the Kurtosis which was 12.732 > 3 which is the normal value indicates that the

degree of peakedness within the period of this study was not normally distributed as most of the

values did not hover around the mean. The Jarque-Bera statistic is an indication of the normality

of distributions was 1511.95 and since the probability was equal to zero, the distribution was not

normally distributed.

Again as shown by the table, the average assets structure of Nigerian manufacturing firms for the

period was 6.3067 while the median was 6.18. The maximum assets structure was 9.18 while the

minimum was N3.80 with a standard deviation of 1.0178. As revealed by the skewness, there

was a positive skewness (0.0768) of asset structure indicating that the degree of departure from

96

the mean of the distribution is positive revealing that overall there was a consistent increase in

assets structure from 1999 to 2012. As indicated by the Kurtosis which was 2.7068 < 3 which is

the normal value indicates that the degree of peakedness within the period of this study was

normally distributed as most of the values hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 1.233. The probability value of 0.53 reveals that

53% of normality can be explained hence, the distribution was normally distributed.

The average size of Nigerian manufacturing firms for the period was 5.53 while the median was

5.555. The maximum size of Nigerian manufacturing firms for the period of this study was 7.86

while the minimum was 1.60 with a standard deviation of 1.009. As revealed by the skewness,

there was a negative skewness (-0.227) of size indicating that the degree of departure from the

mean of the distribution is negative revealing that overall there was a consistent decrease in size

from 1999 to 2012. As indicated by the Kurtosis which was 3.253 > 3 which is the normal value

indicates that the degree of peakedness within the period of this study was not normally

distributed as most of the values did not hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 3.253. The probability value of 0.19 reveals that

19% of normality can be explained hence, the distribution was not normally distributed.

Figure 4.1 diagrammatically presents external finance, earnings per share, asset structure and

total size of Nigerian manufacturing firms from 1999 to 2012

97

Figure 4.1: External Finance, Earnings per Share Asset structure and Size

Source: Researcher’s E-view Result

(b) Objective Two: To examine the impact of external financing on the payout ratio of the quoted manufacturing firms in Nigeria

Table 4.1 and 4.2 present the abridged data and descriptive statistics for the period 1999 to 2012.

As revealed from the tables, it showed that the mean of the external finance of quoted Nigerian

manufacturing firms was 0.8425 while the median was 0.1600. As revealed by the skewness,

there was a positive skewness (10.37) of external finance indicating that the degree of departure

from the mean of the distribution is positive revealing that overall there was a consistent increase

in external finance from 1999 to 2012. Though as indicated by the Kurtosis which was 115.37 >

3 which is the normal value indicates that the degree of peakedness within the period of this

study were not normally distributed as most of the values did not hover around the mean. The

Jarque-Bera statistic is an indication of the normality of distributions was 146918 and since the

probability was equal to zero, the distribution was not normally distributed.

From the table also, the average dividend per share of Nigerian manufacturing firms was N3.74k

while the median was N0.69k. The maximum dividend per share was N40.00k while the

minimum was N0.01k with a standard deviation of 8.25. As revealed by the skewness, there was

98

a positive skewness (3.009) of dividend per share indicating that the degree of departure from the

mean of the distribution is positive revealing that overall there was a consistent increase in

dividend per share from 1999 to 2012. As indicated by the Kurtosis which was 11.49 > 3 which

is the normal value indicates that the degree of peakedness within the period of this study was

not normally distributed as most of the values did not hover around the mean. The Jarque-Bera

statistic is an indication of the normality of distributions was 1218.67 and since the probability

was equal to zero, the distribution was not normally distributed.

From the table also, the average assets structure of Nigerian manufacturing firms for the period

was 6.3067 while the median was 6.18. The maximum assets structure was 9.18 while the

minimum was N3.80 with a standard deviation of 1.0178. As revealed by the skewness, there

was a positive skewness (0.0768) of asset structure indicating that the degree of departure from

the mean of the distribution is positive revealing that overall there was a consistent increase in

assets structure from 1999 to 2012. As indicated by the Kurtosis which was 2.7068 < 3 which is

the normal value indicates that the degree of peakedness within the period of this study was

normally distributed as most of the values hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 1.233. The probability value of 0.53 reveals that

53% of normality can be explained hence, the distribution was normally distributed.

The average size of Nigerian manufacturing firms for the period was 5.53 while the median was

5.555. The maximum size of Nigerian manufacturing firms for the period of this study was 7.86

while the minimum was 1.60 with a standard deviation of 1.009. As revealed by the skewness,

there was a negative skewness (-0.227) of size indicating that the degree of departure from the

mean of the distribution is negative revealing that overall there was a consistent decrease in size

from 1999 to 2012. As indicated by the Kurtosis which was 3.253 > 3 which is the normal value

indicates that the degree of peakedness within the period of this study was not normally

distributed as most of the values did not hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 3.253. The probability value of 0.19 reveals that

19% of normality can be explained hence, the distribution was not normally distributed.

99

Figure 4.2 diagrammatically presents external finance, payout ratio, asset structure and total size

of Nigerian manufacturing firms from 1999 to 2012

Figure 4.2: External Finance, Pay-out Ratio, Asset structure and Size

Source: Researcher’s E-view Result

(c) Objective Three: To examine the impact of external financing on the dividend per share of the quoted manufacturing firms in Nigeria

Table 4.1 and 4.2 present the abridged data and descriptive statistics for the period 1999 to 2012.

As revealed from the tables, it showed that the mean of the external finance of quoted Nigerian

manufacturing firms was 0.8425 while the median was 0.1600. As revealed by the skewness,

there was a positive skewness (10.37) of external finance indicating that the degree of departure

from the mean of the distribution is positive revealing that overall there was a consistent increase

in external finance from 1999 to 2012. Though as indicated by the Kurtosis which was 115.37 >

3 which is the normal value indicates that the degree of peakedness within the period of this

study were not normally distributed as most of the values did not hover around the mean. The

Jarque-Bera statistic is an indication of the normality of distributions was 146918 and since the

probability was equal to zero, the distribution was not normally distributed.

100

From the table also, the average payout ratio of Nigerian manufacturing firms for the period was

4.56 while the median was 1.94. The maximum earnings per share were N136.50 while the

minimum was 0.01 with a standard deviation of 10.76. As revealed by the skewness, there was a

positive skewness of 8.211 indicating that the degree of departure from the mean of the

distribution is positive revealing that overall there was a consistent increase in payout ratio from

1999 to 2012. As indicated by the Kurtosis which was 90.24 > 3 which is the normal value

indicates that the degree of peakedness within the period of this study was not normally

distributed as most of the values did not hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 88666.79 and since the probability was equal to

zero, the distribution was not normally distributed.

From the table also, the average assets structure of Nigerian manufacturing firms for the period

was 6.3067 while the median was 6.18. The maximum assets structure was 9.18 while the

minimum was N3.80 with a standard deviation of 1.0178. As revealed by the skewness, there

was a positive skewness (0.0768) of asset structure indicating that the degree of departure from

the mean of the distribution is positive revealing that overall there was a consistent increase in

assets structure from 1999 to 2012. As indicated by the Kurtosis which was 2.7068 < 3 which is

the normal value indicates that the degree of peakedness within the period of this study was

normally distributed as most of the values hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 1.233. The probability value of 0.53 reveals that

53% of normality can be explained hence, the distribution was normally distributed.

The average size of Nigerian manufacturing firms for the period was 5.53 while the median was

5.555. The maximum size of Nigerian manufacturing firms for the period of this study was 7.86

while the minimum was 1.60 with a standard deviation of 1.009. As revealed by the skewness,

there was a negative skewness (-0.227) of size indicating that the degree of departure from the

mean of the distribution is negative revealing that overall there was a consistent decrease in size

from 1999 to 2012. As indicated by the Kurtosis which was 3.253 > 3 which is the normal value

indicates that the degree of peakedness within the period of this study was not normally

distributed as most of the values did not hover around the mean. The Jarque-Bera statistic is an

101

indication of the normality of distributions was 3.253. The probability value of 0.19 reveals that

19% of normality can be explained hence, the distribution was not normally distributed.

Figure 4.3 diagrammatically presents external finance, dividend per share, asset structure and

total size of Nigerian manufacturing firms from 1999 to 2012

Figure 4.3: External Finance, Dividend per Share, Asset structure and Size

Source: Researcher’s E-view Result

(d) Objective Four: To examine the impact of external financing on the return on assets of the quoted manufacturing firms in Nigeria

Table 4.1 and 4.2 present the abridged data and descriptive statistics for the period 1999 to 2012.

As revealed from the tables, it showed that the mean of the external finance of quoted Nigerian

manufacturing firms was 0.8425 while the median was 0.1600. As revealed by the skewness,

there was a positive skewness (10.37) of external finance indicating that the degree of departure

from the mean of the distribution is positive revealing that overall there was a consistent increase

in external finance from 1999 to 2012. Though as indicated by the Kurtosis which was 115.37 >

3 which is the normal value indicates that the degree of peakedness within the period of this

study were not normally distributed as most of the values did not hover around the mean. The

102

Jarque-Bera statistic is an indication of the normality of distributions was 146918 and since the

probability was equal to zero, the distribution was not normally distributed.

The average return on assets is 0.557 while the median was 0.275. The maximum return on

assets was 17.22 while the minimum was 0.00 with a standard deviation was 1.32. As revealed

by the skewness, there was a positive skewness of 9.012 indicating that the degree of departure

from the mean of the distribution is positive revealing that overall there was a consistent increase

in return on assets from 1999 to 2012. As indicated by the Kurtosis which was 103.16 > 3 which

is the normal value indicates that the degree of peakedness within the period of this study was

not normally distributed as most of the values did not hover around the mean. The Jarque-Bera

statistic is an indication of the normality of distributions was 116505.9 and since the probability

was equal to zero, the distribution was not normally distributed.

From the table also, the average assets structure of Nigerian manufacturing firms for the period

was 6.3067 while the median was 6.18. The maximum assets structure was 9.18 while the

minimum was N3.80 with a standard deviation of 1.0178. As revealed by the skewness, there

was a positive skewness (0.0768) of asset structure indicating that the degree of departure from

the mean of the distribution is positive revealing that overall there was a consistent increase in

assets structure from 1999 to 2012. As indicated by the Kurtosis which was 2.7068 < 3 which is

the normal value indicates that the degree of peakedness within the period of this study was

normally distributed as most of the values hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 1.233. The probability value of 0.53 reveals that

53% of normality can be explained hence, the distribution was normally distributed.

The average size of Nigerian manufacturing firms for the period was 5.53 while the median was

5.555. The maximum size of Nigerian manufacturing firms for the period of this study was 7.86

while the minimum was 1.60 with a standard deviation of 1.009. As revealed by the skewness,

there was a negative skewness (-0.227) of size indicating that the degree of departure from the

mean of the distribution is negative revealing that overall there was a consistent decrease in size

from 1999 to 2012. As indicated by the Kurtosis which was 3.253 > 3 which is the normal value

103

indicates that the degree of peakedness within the period of this study was not normally

distributed as most of the values did not hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 3.253. The probability value of 0.19 reveals that

19% of normality can be explained hence, the distribution was not normally distributed.

Figure 4.4 diagrammatically presents external finance, return on assets, asset structure and total

size of Nigerian manufacturing firms from 1999 to 2012

Figure 4.4: External Finance, Return on Assets, Asset structure and Size

Source: Researcher’s E-view Result

(e) Objective Five: To examine the impact of external financing on the return on equity of the quoted manufacturing firms in Nigeria

Table 4.1 and 4.2 present the abridged data and descriptive statistics for the period 1999 to 2012.

As revealed from the tables, it showed that the mean of the external finance of quoted Nigerian

manufacturing firms was 0.8425 while the median was 0.1600. As revealed by the skewness,

there was a positive skewness (10.37) of external finance indicating that the degree of departure

from the mean of the distribution is positive revealing that overall there was a consistent increase

in external finance from 1999 to 2012. Though as indicated by the Kurtosis which was 115.37 >

3 which is the normal value indicates that the degree of peakedness within the period of this

study were not normally distributed as most of the values did not hover around the mean. The

104

Jarque-Bera statistic is an indication of the normality of distributions was 146918 and since the

probability was equal to zero, the distribution was not normally distributed.

From the table also, the average return on equity is 0.415 while the median was 0.260. The

maximum return on equity was 21.03 while the minimum was -9.42 with a standard deviation of

1.75. As revealed by the skewness, there was a positive skewness of 5.96 indicating that the

degree of departure from the mean of the distribution is positive revealing that overall there was

a consistent increase in return on equity from 1999 to 2012. As indicated by the Kurtosis which

was 78.70 > 3 which is the normal value indicates that the degree of peakedness within the

period of this study was not normally distributed as most of the values did not hover around the

mean. The Jarque-Bera statistic is an indication of the normality of distributions was 66069.76

and since the probability was equal to zero, the distribution was not normally distributed.

From the table also, the average assets structure of Nigerian manufacturing firms for the period

was 6.3067 while the median was 6.18. The maximum assets structure was 9.18 while the

minimum was N3.80 with a standard deviation of 1.0178. As revealed by the skewness, there

was a positive skewness (0.0768) of asset structure indicating that the degree of departure from

the mean of the distribution is positive revealing that overall there was a consistent increase in

assets structure from 1999 to 2012. As indicated by the Kurtosis which was 2.7068 < 3 which is

the normal value indicates that the degree of peakedness within the period of this study was

normally distributed as most of the values hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 1.233. The probability value of 0.53 reveals that

53% of normality can be explained hence, the distribution was normally distributed.

The average size of Nigerian manufacturing firms for the period was 5.53 while the median was

5.555. The maximum size of Nigerian manufacturing firms for the period of this study was 7.86

while the minimum was 1.60 with a standard deviation of 1.009. As revealed by the skewness,

there was a negative skewness (-0.227) of size indicating that the degree of departure from the

mean of the distribution is negative revealing that overall there was a consistent decrease in size

from 1999 to 2012. As indicated by the Kurtosis which was 3.253 > 3 which is the normal value

indicates that the degree of peakedness within the period of this study was not normally

105

distributed as most of the values did not hover around the mean. The Jarque-Bera statistic is an

indication of the normality of distributions was 3.253. The probability value of 0.19 reveals that

19% of normality can be explained hence, the distribution was not normally distributed.

Figure 4.5 diagrammatically presents external finance, return on equity, asset structure and total

size of Nigerian manufacturing firms from 1999 to 2012

Figure 4.5: External Finance, Return on Equity, Asset structure and Size

Source: Researcher’s E-view Result

4.2 Test of Hypotheses

The hypotheses stated were tested using four steps. In step one; we restated the hypotheses in

null and alternate forms. In step two, we compare the random effect and fixed effect regression

results to ascertain the choice of result to use for the analysis. In Step three, we presented and

analyze the regression result while in step four, decision is made. It is however noted that our

decision rule for this study is to reject the null hypothesis and accept the alternate, otherwise

accept if p value < 0.05.

106

4.2.1 Test of Hypothesis One

Step One: Restatement of the Hypothesis in Null and Alternate forms:

Ho1: External Financing does not have positive and significant impact on earnings per share

of quoted Nigerian manufacturing firms.

Ho1: External Financing has positive and significant impact on earnings per share of quoted

Nigerian manufacturing firms.

Step Two: Comparism of Random and Fixed Effect

Table 4.3 presents the Hausman test summary result of the random and fixed effect.

Table 4.3 Hausman Test Result of Hypothesis One

Prob>chi2 = 0.3941

= 2.98

chi2(3) = (b-B)'[(V_b-V_B)^(-1)]( b-B)

Test: Ho: difference in coefficients not syst ematic

B = inconsistent under Ha, efficient un der Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

SZ 1.557088 1.581759 -.0246711 .2 830469

LogAS -2.273572 -3.206971 .9333993 .5 480008

EXF -.0043609 -.010167 .0058061 .0 089396

fixed random Difference S.E.

(b) (B) (b-B) sqrt(di ag(V_b-V_B))

Coefficients

Source: Researcher’s Stata Result

From the above, the null hypothesis is rejected since p-value > 0.05, hence, the random effect regression model was used to test hypothesis one.

Step Three: Analysis of Regression Result of Hypothesis One

Table 4.4 presents the regression results of hypothesis one.

107

Table 4.4 Regression Result of Hypothesis One

rho 0 (fraction of variance due to u_i)

sigma_e 35.97919

sigma_u 0

_cons 20.43152 9.421277 2.17 0.030 1.96615 8 38.89688

SZ 1.581759 1.746439 0.91 0.365 -1.84119 8 5.004717

LogAS -3.206971 2.052298 -1.56 0.118 -7.22940 1 .8154581

EXF -.010167 .0481042 -0.21 0.833 -.104449 5 .0841155

EPS Coef. Std. Err. z P>|z| [95% Co nf. Interval]

corr(u_i, X) = 0 (assumed) Pro b > chi2 = 0.4737

Wal d chi2(3) = 2.51

overall = 0.0042 max = 51

between = 0.1289 avg = 31.7

R-sq: within = 0.0019 Obs per group: min = 1

Group variable: YEAR Num ber of groups = 19

Random-effects GLS regression Num ber of obs = 603

Source: Researcher’s Stata Result

As revealed from table 4.4, the impact of the external financing on earnings per share of quoted

Nigerian manufacturing firms is negative and non-significant (α = -.01, z = -0.21, p-value 0.833

> 0.05). This indicates that the use of external financing does not impact positively on the

earnings per share of Nigerian manufacturing firms. Overall, the coefficient of determination as

revealed by R-square (R2) in between the firms was 12.8%. This indicates that 12.8% of

variations observed in the dependent variable earnings per share were explained by variations in

the independent variable external financing and the control variables (asset structure and size).

This is understandable given the level of observation in the panel. The Wald chi2 which was 2.51

> 0.05 indicates that the F-test result of all the coefficients in the model are not different than

zero. The random effect result which was equal zero reveals that the differences across units are

uncorrelated with the regressors. For the control variables, the results indicates that asset

structure of quoted manufacturing firms in Nigeria also had negative and non-significant (α = -

3.21, z = -1.56, p-value 0.118 > 0.05) impact on earnings per share while size of the firm had

positive though non-significant (α = 1.58, z = 0.91, p-value 0.365 > 0.05) impact on earnings per

share.

108

Step Four: Decision

Based on the result of the hypothesis test in step three, the null hypothesis is accepted and the

alternate rejected indicating that external financing does not have positive and significant impact

on earnings per share of quoted Nigerian manufacturing firms.

4.2.2 Test of Hypothesis Two

Step One: Restatement of Hypothesis in Null and Alternate forms

Ho2: External Financing does not have positive and significant impact on pay-out ratio of

quoted Nigerian manufacturing firms.

Ho3: External Financing has positive and significant impact on pay-out ratio of quoted

Nigerian manufacturing firms.

Step Two: Comparism of Random and Fixed Effect

Table 4.5 presents the comparism results of the random and fixed effect regression model.

Table 4.5 Hausman Test Result of Hypothesis Two

Prob>chi2 = 0.9262

= 0.47

chi2(3) = (b-B)'[(V_b-V_B)^(-1)]( b-B)

Test: Ho: difference in coefficients not syst ematic

B = inconsistent under Ha, efficient un der Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

SZ .6469668 .6852343 -.0382675 .1 292056

LogAS -1.169622 -1.31748 .1478582 .2 293458

EXF -.0953383 -.111698 .0163597 .0 315287

fixed random Difference S.E.

(b) (B) (b-B) sqrt(di ag(V_b-V_B))

Coefficients

. hausman fixed random

Source: Researcher’s Stata Result

From the above, the null hypothesis is rejected since p-value > 0.05, hence, the random effect regression model was used to test hypothesis two.

109

Step Three: Analysis of Regression Result of Hypothesis Two

Table 4.6 presents the regression results of hypothesis two.

Table 4.6 Regression Result of Hypothesis Two

rho .2414419 (fraction of variance due to u_i)

sigma_e 10.532705

sigma_u 5.9422622

_cons 9.644671 4.814652 2.00 0.045 .208125 8 19.08122

SZ .6852343 .7248161 0.95 0.344 -.735379 1 2.105848

LogAS -1.31748 .8916331 -1.48 0.140 -3.06504 9 .4300885

EXF -.111698 .1616554 -0.69 0.490 -.428536 8 .2051407

POR Coef. Std. Err. z P>|z| [95% Co nf. Interval]

corr(u_i, X) = 0 (assumed) Pro b > chi2 = 0.5240

Wal d chi2(3) = 2.24

overall = 0.0140 max = 22

between = 0.1078 avg = 16.1

R-sq: within = 0.0065 Obs per group: min = 2

Group variable: YEAR Num ber of groups = 17

Random-effects GLS regression Num ber of obs = 274

Source: Researcher’s Stata Result

As revealed from table 4.6, the impact of the external financing on pay-out ratio of quoted

Nigerian manufacturing firms is negative and non-significant (α = -.11, z = -0.69, p-value 0.49 >

0.05). This indicates that the use of external financing does not impact positively on the pay-out

ratio of Nigerian manufacturing firms. Overall, the coefficient of determination as revealed by R-

square (R2) in between the firms was 10.8%. This indicates that 10.8% of variations observed in

the dependent variable pay-out ratio were explained by variations in the independent variable

external financing and the control variables (asset structure and size). This is understandable

given the level of observation in the panel. The Wald chi2 which was 2.24 > 0.05 indicates that

the F-test result of all the coefficients in the model are not different than zero. The random effect

result which was equal zero reveals that the differences across units are uncorrelated with the

regressors. For the control variables, the results indicates that asset structure of quoted

manufacturing firms in Nigeria also had negative and non-significant (α = -1.32, z = -1.48, p-

value 0.140 > 0.05) impact on pay-out ratio while size of the firm had positive though non-

significant (α = 0.69, z = 0.95, p-value 0.344 > 0.05) impact on pay-out ratio.

110

Step Four: Decision

From the result of the hypothesis tested, the null hypothesis is accepted while the alternate

hypothesis rejected hence, external financing does not have positive and significant impact on

pay-out ratio of quoted Nigerian manufacturing firms.

4.2.3 Test of Hypothesis Three

Step One: Restatement of Hypothesis in Null and Alternate forms

Ho3: External Financing does not have positive and significant impact on dividend per share

of quoted Nigerian manufacturing firms.

Ha3: External Financing has positive and significant impact on dividend per share of quoted

Nigerian manufacturing firms.

Step Two: Comparism of Random and Fixed Effect

Table 4.7 presents the comparism results of the random and fixed effect regression model.

Table 4.7: Hausman Test Result of Hypothesis Three

(V_b-V_B is not positive definite)

Prob>chi2 = 0.0000

= 29.65

chi2(3) = (b-B)'[(V_b-V_B)^(-1)]( b-B)

Test: Ho: difference in coefficients not syst ematic

B = inconsistent under Ha, efficient un der Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

SZ .2786763 .2076891 .0709871 .

LogAS -.8213673 -1.1736 .3522326 . 056826

EXF -.0036115 -.006969 .0033575 .

fixed random Difference S.E.

(b) (B) (b-B) sqrt(di ag(V_b-V_B))

Coefficients

. hausman fixed random

Source: Researcher’s Stata Result

From the above, the null hypothesis is accepted since p-value < 0.05, hence, the fixed effect regression model was used to test hypothesis three.

111

Step Three: Analysis of Regression Result of Hypothesis Three

Table 4.8 presents the regression result of hypothesis three.

Table 4.8 Regression Result of Hypothesis Three

F test that all u_i=0: F(18, 581) = 2.07 Prob > F = 0.0058

rho .08267882 (fraction of variance due to u_i)

sigma_e 5.655876

sigma_u 1.6979946

_cons 5.34795 1.583215 3.38 0.001 2.23842 7 8.457473

SZ .2786763 .2781199 1.00 0.317 -.267566 7 .8249192

LogAS -.8213673 .3339213 -2.46 0.014 -1.47720 7 -.1655273

EXF -.0036115 .0076914 -0.47 0.639 -.018717 8 .0114948

DPS Coef. Std. Err. t P>|t| [95% Co nf. Interval]

corr(u_i, Xb) = 0.2407 Pro b > F = 0.0806

F(3 ,581) = 2.26

overall = 0.0286 max = 51

between = 0.4293 avg = 31.7

R-sq: within = 0.0115 Obs per group: min = 1

Group variable: YEAR Num ber of groups = 19

Fixed-effects (within) regression Num ber of obs = 603

Source: Researcher’s Stata Result

As revealed from table 4.8, the impact of the external financing on dividend per share of quoted

Nigerian manufacturing firms is negative and non-significant (α = -.003, t = -0.47, p-value 0.639

> 0.05). This indicates that the use of external financing does not impact positively on the

dividend per share of Nigerian manufacturing firms. Overall, the coefficient of determination as

revealed by R-square (R2) in between the firms was 42.9%. This indicates that 42.9% of

variations observed in the dependent variable dividend per share were explained by variations in

the independent variable external financing and the control variables (asset structure and size).

This is understandable given the level of observations in the panel. The Wald Chi2 which was

2.26 > 0.05 indicates that the F-test result of all the coefficients in the model are not different

than zero. The random effect result which was less than zero reveals that the differences across

units are uncorrelated with the regressors. For the control variables, the results indicates that

asset structure of quoted manufacturing firms in Nigeria also had negative and significant (α = -

.82, t = -2.46, p-value 0.014 < 0.05) impact on dividend per share while size of the firm had

positive though non-significant (α = 0.28, t = 1.00, p-value 0.317 > 0.05) impact on dividend per

share.

112

Step Four: Decision

From the result of the hypothesis tested, the null hypothesis is accepted while the alternate

hypothesis rejected hence, external financing does not have positive and significant impact on

dividend per share of quoted Nigerian manufacturing firms.

4.2.4 Test of Hypothesis Four

Step One: Restatement of Hypothesis in Null and Alternate forms

Ho4: External Financing does not have positive and significant impact on return on assets

of quoted Nigerian manufacturing firms.

Ha4: External Financing has positive and significant impact on return on assets of quoted

Nigerian manufacturing firms.

Step Two: Comparism of Random and Fixed Effect

Table 4.9 presents the comparism results of the random and fixed effect regression model.

Table 4.9 Hausman Test Result of Hypothesis Four

Prob>chi2 = 0.0612

= 7.36

chi2(3) = (b-B)'[(V_b-V_B)^(-1)]( b-B)

Test: Ho: difference in coefficients not syst ematic

B = inconsistent under Ha, efficient un der Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

SZ .3842447 .3882986 -.0040539 .0 079892

LogAS -.5090162 -.4676977 -.0413185 .0 161683

EXF .1647409 .1652003 -.0004593 .0 002567

fixed random Difference S.E.

(b) (B) (b-B) sqrt(di ag(V_b-V_B))

Coefficients

. hausman fixed random

Source: Researcher’s Stata Result

From the above, the null hypothesis is accepted since p-value < 0.05, hence, the fixed effect regression model was used to test hypothesis four.

113

Step Three: Analysis of Regression Result of Hypothesis Four

Table 4.10 presents the regression result of hypothesis four.

Table 4.10 Regression Result of Hypothesis Four

F test that all u_i=0: F(18, 581) = 0.85 Prob > F = 0.6355

rho .03852239 (fraction of variance due to u_i)

sigma_e 1.0874706

sigma_u .217673

_cons 1.470548 .3044091 4.83 0.000 .872671 2 2.068424

SZ .3842447 .0534749 7.19 0.000 .279217 1 .4892723

LogAS -.5090162 .064204 -7.93 0.000 -.635116 3 -.3829161

EXF .1647409 .0014788 111.40 0.000 .161836 4 .1676455

ROA Coef. Std. Err. t P>|t| [95% Co nf. Interval]

corr(u_i, Xb) = 0.0370 Pro b > F = 0.0000

F(3 ,581) = 4462.87

overall = 0.9582 max = 51

between = 0.9016 avg = 31.7

R-sq: within = 0.9584 Obs per group: min = 1

Group variable: YEAR Num ber of groups = 19

Fixed-effects (within) regression Num ber of obs = 603

Source: Researcher’s Stata Result

As revealed from table 4.10, the impact of the external financing on return on assets of quoted

Nigerian manufacturing firms is positive and significant (α = 0.16, t = 111.40, p-value 0.00 <

0.05). This indicates that the use of external financing have positive and significant on the return

on assets of Nigerian manufacturing firms. Overall, the coefficient of determination as revealed

by R-square (R2) in between the firms was 95.8%. This indicates that 95.8% of variations

observed in the dependent variable return on assets were explained by variations in the

independent variable external financing and the control variables (asset structure and size). This

is quite significant given the level of observations in the panel. The F-test which was 4462.87

indicates that the result of all the coefficients in the model is perfectly fitted. The fixed effect

result which was less than zero reveals that the differences across units are uncorrelated with the

regressors. For the control variables, the results indicates that asset structure of quoted

manufacturing firms in Nigeria also had negative and significant (α = -0.51, t = -7.93, p-value

0.000 < 0.05) impact on return on assets while size of the firm had positive though positive and

significant (α = 0.38, t = 0.05, p-value 0.000 < 0.05) impact on return on assets.

114

Step Four: Decision

From the result of the hypothesis tested, the null hypothesis is rejected while the alternate

hypothesis accepted hence; external financing have positive and significant impact on return on

assets of quoted Nigerian manufacturing firms.

4.2.5 Test of Hypothesis Five

Step One: Restatement of Hypothesis in Null and Alternate Form

Ho5: External Financing does not have positive and significant impact on return on equity

of quoted Nigerian manufacturing firms.

Ha5: External Financing has positive and significant impact on return on equity of quoted

Nigerian manufacturing firms.

Step Two: Comparism of Random and Fixed Effect

Table 4.11 presents the comparism results of the random and fixed effect regression model.

Table 4.11 Hausman Test Result of Hypothesis Five

Prob>chi2 = 0.7148

= 1.36

chi2(3) = (b-B)'[(V_b-V_B)^(-1)]( b-B)

Test: Ho: difference in coefficients not syst ematic

B = inconsistent under Ha, efficient un der Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

SZ .2586032 .2637706 -.0051674 .0 135227

LogAS -.3544257 -.3427512 -.0116745 .0 265813

EXF -.0023573 -.0026206 .0002633 .0 004296

fixed random Difference S.E.

(b) (B) (b-B) sqrt(di ag(V_b-V_B))

Coefficients

. hausman fixed random

Source: Researcher’s Stata Result

From the above, the null hypothesis isrejected since p-value > 0.05, hence, the random effect regression model was used to test hypothesis five.

115

Step Three: Analysis of Regression Result of Hypothesis Five

Table 4.12 presents the regression result of hypothesis five.

Table 4.12 Regression Result of Hypothesis Five

rho 0 (fraction of variance due to u_i)

sigma_e 1.7603527

sigma_u 0

_cons 1.115163 .461236 2.42 0.016 .211156 5 2.019169

SZ .2637706 .0855002 3.09 0.002 .096193 4 .4313478

LogAS -.3427512 .100474 -3.41 0.001 -.539676 7 -.1458257

EXF -.0026206 .002355 -1.11 0.266 -.007236 4 .0019951

ROE Coef. Std. Err. z P>|z| [95% Co nf. Interval]

corr(u_i, X) = 0 (assumed) Pro b > chi2 = 0.0049

Wal d chi2(3) = 12.88

overall = 0.0211 max = 51

between = 0.2066 avg = 31.7

R-sq: within = 0.0212 Obs per group: min = 1

Group variable: YEAR Num ber of groups = 19

Random-effects GLS regression Num ber of obs = 603

. xtreg ROE EXF LogAS SZ, re

Source: Researcher’s Stata Result

As revealed from table 4.12, the impact of the external financing on return on equity of quoted

Nigerian manufacturing firms is negative and non-significant (α = -0.002, z = -1.11, p-value

0.266 > 0.05). This indicates that the use of external financing have negative and non-significant

on the return on equity of Nigerian manufacturing firms. Overall, the coefficient of

determination as revealed by R-square (R2) in between the firms was 20.67%. This indicates that

20.67% of variations observed in the dependent variable return on equity were explained by

variations in the independent variable external financing and the control variables (asset structure

and size). This is understandable given the level of observations in the panel data set. The Wald

Chi2 which was 12.88 > 0.05 indicates that the F-test result of all the coefficients in the model is

not different than zero. The random effect result which was less than zero reveals that the

differences across units are uncorrelated with the regressors. For the control variables, the results

indicates that asset structure of quoted manufacturing firms in Nigeria also had negative and

significant (α = -.34.3, t = -3.41, p-value 0.001 < 0.05) impact on return on equity while size of

the firm had positive though positive and significant (α = 0.26, t = 3.09, p-value 0.002 < 0.05)

impact on return on equity.

116

Step Four: Decision

From the result of the hypothesis tested, the null hypothesis is accepted while the alternate

hypothesis rejected hence; external financing does not have positive and significant impact on

return on equity of quoted Nigerian manufacturing firms.

4.3 Implications of Results

As observed from the result of this study, external financing had negative and non-significant

impact on earnings per share, payout ratio, dividend per share and return on equity while it had

positive and significant on return on assets. This is in line with various findings in other

jurisdictions.

For instance, Asif, Rasool and Kamal (2011) examine the relationship between dividend policy

and financial leverage of 403 companies listed with Karachi Stock Exchange during the period

2002 to 2008. They say dividend policy, vastly followed by the companies, was tested by

applying the extended model of Linter (1956) with the debt ratio of the firm, the previous year’s

dividend yield as its independent variables and change in earnings as a dummy variable.

Descriptive statistics for their entire variables were calculated and then correlation matrix was

calculated to identify the preliminary relationship among all the variables, followed by

regression analysis on panel data to examine the significance and magnitude through fixed and

random effects models. Theoretical assertions were justified through random effect model that

the level of corporate debt (leverage) and widely practiced dividend policy, significantly, affect

the dividend policy of the Pakistani firms. On the other hand, financial leverage was found to

have a negative impact on dividend payout, indicating less dividend payments by high-debt

firms. The above findings suggest that change external financing has no significant impact on

dividend policy in case of Pakistani firms.

Again, Khan (2010) explored the relationship of capital structure decision with the performance

of the firms in the developing market economies like Pakistan. The Pooled Ordinary Least

Square regression was applied to 36 engineering sector firms in Pakistani market listed on the

Karachi Stock Exchange (KSE) during the period 2003-2009. The results show that financial

117

leverage measured by short term debt to total assets (STDTA) and total debt to total assets

(TDTA) has a significantly negative relationship with the firm performance measured by Return

on Assets (ROA), Gross Profit Margin (GM) and Tobin’s Q. The relationship between financial

leverage and firm performance measured by the return on equity (ROE) is negative but

insignificant. Asset size has an insignificant relationship with the firm performance measured by

ROA and GM but negative and significant relationship exists with Tobin’s Q. Like Nigerian

firms, which largely depends on Firms in the engineering sector of Pakistan are largely

dependent on short term debt but debts are attached with strong covenants which affect the

performance of the firm.

This finding is consistent with the work of Olowoniyi and Ojenike (2011), Elsas, Flannery and

Garfinkel (2006) and Wang et al (2010). This suggests that the external finance though is

expected to have positively enhanced growth and size; however, it negatively impacted on stock

return of listed firms. Efforts at improving size of the firms and adjustment of firms’ tangibility

to a positive side is suggested to improve financial situation of firms through stock return.

Furthermore, Elsas, Flannery and Garfinkel (2006) assemble a sample of 1,558 large investments

made by 1,185 firms over the period 1989-1999, and raises two main issues, firstly how do firms

pay for these large investments? and how does the stock market subsequently evaluate them?

They found that major investments are mostly externally financed. The pecking order and market

timing effects on capital structure are transitory. Firms move toward target leverage ratios. Long-

run abnormal stock returns are not generally consistent with the hypothesis that managers tend to

overinvest with internal funds and they again argued that only firms financing large projects with

(newly-raised) external funds exhibit reliably negative abnormal returns over the subsequent 1 –

3 years.

In line with the above work, Wang et al (2010) study and compare the determinants of stock

returns in the 1987 and 2008 stock market meltdowns with the multivariate regression analysis

technique. They found that technical insolvency risk and bankruptcy risk were significant

determinants of stock returns in the 2008 market meltdown. They say investors were also

118

somewhat concerned with bankruptcy risk in the 1987 market meltdown. However, technical

insolvency risk was not a significant determinant of stock returns in the 1987 meltdown. Their

findings indicate that stocks with higher betas, larger market cap, and greater return volatility lost

more value in both meltdowns. They found the market-to-book ratio to be a significant

determinant of stock returns in the 2008 meltdown but not in the 1987 meltdown. Their study

also found that stock illiquidity to be a significant determinant of stock returns in the 1987

meltdown but not in the 2008 meltdown. With data for two most important stock market

meltdowns in U.S. history since the Great Depression.

Finally, the implication of the finding indicates that external financing does not magnified

earnings attributable to shares both in terms of the book value measures or returns attributable to

them; however, it increases the assets structure of these firms.

119

REFERENCES

Asif, A., W. Rasool & Kamal, Y (2011). Impact of financial leverage on dividend policy: empirical evidence from Karachi stock exchange-listed companies. African Journal of Business Management, 5(4)1312-1324 Elsas, R., M.J. Flannery & Garfinkel, J.A (2006). major investments, firm financing decisions, and long-run performance. Journal of Finance, 52(3)2345-2367 Khan, A.G (2010). The relationship of capital structure decisions with firm performance: a study of the engineering sector of Pakistan. Pakistan Economic Review, 21(5)93-123 5 Linter, L (1956). External financing: A survey. Journal of Accounting and Economics, 1(3)48-65 Olowoniyi A. O & Ojenike J.O (2012). Determinants of stock return of Nigerian-listed firms. Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 3(4)389-392 Wang, J, G. Meric, Z. Liu & Meric, L (2010). A comparison of the determinants of stock returns in the 1987 and 2008 stock market meltdowns. Banking and Finance Review, 13(1)45-77

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CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

5.1 Summary of Findings

From the specific objectives of this study and the hypotheses tested, the following are the

summary of findings of this study.

1. The impact of the external financing on earnings per share of quoted Nigerian

manufacturing firms is negative and non-significant. For the control variables, the results

indicates that asset structure of quoted manufacturing firms in Nigeria also had negative

and non-significant impact on earnings per share while size of the firm had positive

though non-significant impact on earnings per share.

2. External financing had negative and non-significant impact on pay-out ratio of quoted

Nigerian manufacturing firms. The results indicates that asset structure of quoted

manufacturing firms in Nigeria also had negative and non-significant impact on pay-out

ratio while size of the firm had positive though non-significant impact on pay-out ratio.

3. The impact of the external financing on dividend per share of quoted Nigerian

manufacturing firms was also negative and non-significant. For the control variables, the

results indicates that asset structure of quoted manufacturing firms in Nigeria also had

negative and significant impact on dividend per share while size of the firm had positive

though non-significant impact on dividend per share.

4. As revealed the hypothesis tested, the impact of the external financing on return on assets

of quoted Nigerian manufacturing firms is positive and significant. For the control

variables, the results indicates that asset structure of quoted manufacturing firms in

Nigeria also had negative and significant impact on return on assets while size of the firm

had positive though positive and significant impact on return on assets.

5. Lastly, the external financing on return on equity of quoted Nigerian manufacturing firms

is negative and non-significant. For the control variables, the results indicates that asset

structure of quoted manufacturing firms in Nigeria also had negative and significant

impact on return on equity while size of the firm had positive though positive and

significant impact on return on equity.

121

5.2 Conclusion

In most developing economies like Nigeria, the financing policies of firms may become relevant

because managers in a company invest in new plants and equipment to generate additional

revenue. This revenue generated belongs to the owners of the company and can be distributed as

either dividend paid to owners or retained in the firm as retained earnings. The retained earnings

could be used for new investment or capitalized by using it to issue bonus shares. Where the

retained earnings are not enough to support all profitable investments opportunities, the company

may forgo the investment or raise additional capital, thus altering the capital structure of firms.

Unlike developed economies where the capital structure of firms comprise of equity and debt,

the capital structure of firms in most developing economies is mainly equity based and where

debt component is involved, it is usually from deposit money banks or other such financial

institutions.

The effect of external financing on firm performance in developing economies like Nigeria could

be explained through several theories such as Miller and Modigliani irrelevance theory, the

pecking order theory, the trade-off theory, the signally hypothesis, market mutation hypothesis

and the agency theory, amongst other capital structure theories. From these theories, the use of

external financing increases returns on equity up to a certain level of operating income not only

in a developing economy like Nigeria but also firms in developed economies. Hence, as the firm

grow; higher levels of external financing are needed to cover for available investment

opportunities. In a perfect world, management would favour more external financing whenever

return on capital exceeds the cost of internal financing. However, higher returns could also result

in higher risk to the business.

The use of external financing is a balancing act between higher returns for shareholders versus

higher risk to shareholders. Though external financing can boost stock performance of firms, it is

still inconclusive as to its impact on performance of firms in developing economies like Nigeria.

It is, therefore, against this background that this study sought to investigate the impact of

external financing on earnings per share of manufacturing firms in Nigeria; determine the impact

of external financing on pay-out ratio of manufacturing firms in Nigeria; examine the impact of

external financing on dividend per share of manufacturing firms in Nigeria; examine the impact

122

of external financing on return on equity of manufacturing firms in Nigeria, and determine return

the impact of external financing on investment of Nigerian manufacturing firms.

The study adopted the ex-post facto research design. Panel time’s series and cross sectional data

were collated from the Annual financial Statement of Quoted Manufacturing firms as well as

from the Nigerian Stock Exchange Factbook for the period 1987 - 2012. Five (5) hypotheses

which state that external financing does not have positive and significant impact on earnings per

share; pay-out ratio, dividend per share, return on equity, and return on investment of quoted

manufacturing firms in Nigeria were formulated where external financing (EF) was adopted as

the independent variable and earnings per share (EPS), pay-out ratio (PR), return on equity

(ROE) and return on investment (ROI)were the dependent variables for the hypotheses

respectively were tested using the Ordinary Least Squares (OLS). Asset structure (AS), firm size

(FS) and firm growth (FG) were introduced as control variables.

The result of this study revealed that external financing had negative and non-significant impact

on earnings per share, payout ratio, dividend per share and return on equity while it had positive

and significant on return on assets.

5.3 Recommendations

In view of the finding of this research, the financial decision which the firm makes must enhance

value for shareholders, potential investors and stakeholders involved with the firm. Also, as a

going concern, it is the wish of investors and investees that the firm should continually exist;

therefore, the financial decision of the firm should ultimately help in achieving the overall

objective of the firm that is, enhancing shareholders wealth maximization. Based on the findings

of this study, the following recommendations were made. There are:

1. Management must match the financing mix to the assets financed as closely as possible in

terms of both timing and cash flows as to achieve the overall objective of the firm

because value enhanced firm implies happy stakeholders thereby enhancing earnings

attributable to shareholders.

123

2. External financing choices can increase the cost of financing with debt instead of equity.

Thus, an increase in debt level in the financial structure of the firm will mean that debt

holders or creditors will have an upper hand in the decision making of the firms with

regards to the strategies adopted by the firm in their investment decisions thus; the use of

external financing can significantly affect the firms’ chances of survival. It is in line with

these findings that the study recommends that appropriate external financings choices

should be made by the firm as to enhance payout ratio of firms.

3. Investors and investees through this study are also reminded of their responsibilities.

Often, it is rare for any firm to depend solely on equity finance in the firms’ financial

structure; therefore, as observed, there are element of debt and equity in the financial mix

of firms. Thus, management may seek other sources of funding which may not be in the

interest of equity holders but may lead to the magnification of returns to equity holders

on overall basis however, as observed in this research; it may come at a cost in extreme

cases such as insolvency and financial distress which may lead to bankruptcy. Therefore,

investees and investors must be patient with management even when return are not made

in the short run as the overall objective of management which is shareholders wealth

maximization may not be immediate but will come in the long run.

4. This study will help management to decide on the optimal debt level that enhances the

value of the firm as well as increase the chances of management to continue in their

position as managers of these firms. The separation of ownerships and management in

modern day corporations (firms) demands that agents must acts in ways that is in line

with the objectives of the principal because failure to do so means the principal (owners)

can remove the agent there limiting efficiency of management. Therefore, the findings of

this research will go a long way in awakening management to their responsibility as

agents by enhancing return on assets.

5. A major significant contribution of this study is to provide an insight to management on

the importance of ensuring that financial decisions made by them should be able to

enhance shareholders’ wealth maximization through the enhancement of return on equity.

The amount of external finance in the financial mix of the firm should be at the optimal

level as to ensure that value is enhanced. Therefore, this study recommends the continual

124

use of external financing up to the optimal level as to improve return on equity of

investors.

5.4 Contributions to Knowledge

Firstly, it has contributed to the volume of researches in this area of corporate finance through

the introduction of a panel data set in determining the impact of external financing on

performance of firms in Nigeria. Thus this study significantly contributed to literature way of

geography in this region of the world.

Secondly, the modification of Abor’s (2008) model to include control variables (firm size and

asset structure) was also a significant contribution to knowledge. The inclusion led to more

robust results which hitherto may not have been possible.

5.5 Recommendation to Further Studies

The recommended areas for further studies are:-

1) The time frame covered by this study can be expanded in future research efforts. The

intention of the researcher was initially to undertake a time frame that could cover the

entire duration of existence of the Nigerian Stock Exchange however; the inability of

the research to gather data for the 10 year studies resulted to the five year period

analyses. Thus, future researcher may increase the time frame.

2) As obtained from the study, the firms studied comprised of selected manufacturing

firms based on availability of data. Future researchers can increase the sample size to

cover firms in all classifications or study all firms.

3) Lastly, it would be wise to replicate the study using comparative data from selected

African or other developing economies.

125

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139

APPENDICES

Appendix 1 Treated Model Data Set COMPANY NAME EXF EPS DPS POR ROA ROE LogAS SZ

UAC PLC(H’M) 0.16 1.99 1.1 1.81 0.10 0.12 7.83 6.50

UAC PLC(H’M) 0.16 3.14 1.3 2.42 0.11 0.13 7.85 6.60

UAC PLC(H’M) 0.16 2.65 2 1.33 0.09 0.11 7.96 6.63

UAC PLC(H’M) 0.16 1.75 1.7 1.03 0.06 0.07 7.85 6.49

UAC PLC(H’M) 0.16 2 1 2 0.28 0.33 7.22 6.51

UAC PLC(H’M) 0.16 1.27 1 1.27 0.31 0.37 7.16 6.21

UAC PLC(H’M) 0.16 1.37 0.85 1.61 0.44 0.52 7.11 6.20

UAC PLC(H’M) 0.16 2.4 0.6 4 0.54 0.64 7.10 6.34

UAC PLC(H’M) 0.16 1.28 0.35 3.66 0.47 0.56 7.07 6.07

UAC PLC(H’M) 0.16 1.11 0.15 7.4 0.74 0.88 6.94 6.00

UAC PLC(H’M) 0.16 0.12 0 1.39 1.64 6.75 6.61

UAC PLC(H’M) 0.16 0.03 0 1.43 1.69 6.68 6.49

UAC PLC(H’M) 0.16 1.76 0.6 2.93 1.25 1.49 6.70 6.68

UAC PLC(H’M) 0.16 0.88 0.6 1.47 0.60 0.71 7.03 6.64

ARBICO 0.16 0.73 0 0.09 0.11 6.09 4.71

ARBICO 0.16 0.01 0 0.02 0.03 6.06 4.10

ARBICO 0.16 0.24 0 0.20 0.24 5.49 4.51

ARBICO 0.16 0.07 0 0.03 0.04 5.51 3.91

ARBICO 0.16 0.01 0 0.11 0.13 5.04 3.93

ARBICO 0.16 1.6 0 0.10 0.12 5.05 3.89

ARBICO 0.16 0.03 0 0.10 0.12 5.09 3.99

ARBICO 0.16 0 0 0.10 0.12 5.12 4.04

ARBICO 0.16 0 0 0.13 0.15 5.07 3.97

ARBICO 0.16 1.6 0 0.44 0.52 4.55 4.05

ARBICO 0.81 0.83 0 0.50 0.59 4.51 4.14

ARBICO 0.47 187.98 0 0.50 0.60 4.52 4.16

ARBICO 0.93 7.87 0 0.50 0.60 4.56 4.23

ARBICO 0.93 32.45 0 0.45 0.54 4.58 4.10

ARBICO 0.93 11.39 0 0.48 0.57 4.59 4.25

CAPPA& D’ALBERTO PLC (N 000)

0.16 2.57 0 0.64 0.76 6.06 5.70

CAPPA& D’ALBERTO PLC (N 000)

0.16 4.25 0.75 5.67 0.13 0.15 5.97 5.92

CAPPA& D’ALBERTO PLC (N 000)

0.16 0.65 0.3 2.17 0.78 0.93 5.81 5.11

CAPPA& D’ALBERTO 0.16 2.01 0.5 4.02 0.42 0.49 5.80 5.30

140

PLC (N 000)

CAPPA& D’ALBERTO PLC (N 000)

0.16 1.28 0.3 4.27 0.24 0.28 5.79 5.10

CAPPA& D’ALBERTO PLC (N 000)

0.16 2.01 0.5 4.02 0.42 0.49 5.80 5.30

CAPPA& D’ALBERTO PLC (N 000)

0.16 1.28 0.3 4.27 0.24 0.28 5.79 5.10

CAPPA& D’ALBERTO PLC (N 000)

0.16 1.13 0.2 5.65 0.24 0.28 5.82 5.05

CAPPA& D’ALBERTO PLC (N 000)

0.16 0 0 0.23 0.28 5.88 5.20

CAPPA& D’ALBERTO PLC (N 000)

0.16 0 0 0.23 0.27 5.92 5.21

CAPPA& D’ALBERTO PLC (N 000)

0.16 1.4 0.4 3.5 0.43 0.51 5.55 5.07

CAPPA& D’ALBERTO PLC (N 000)

0.16 8.12 2.5 3.25 0.30 0.36 5.55 4.84

CAPPA& D’ALBERTO PLC (N 000)

0.16 10.92 0.08 136.5 0.37 0.44 5.53 4.96

CAPPA& D’ALBERTO PLC (N 000)

0.16 10.77 4 2.69 0.34 0.41 5.52 4.96

CAPPA& D’ALBERTO PLC (N 000)

0.16 12.96 2.67 4.85 1.27 1.51 5.05 2.04

COSTAIN 5.02 0.03 0 0.40 0.16 5.13 4.52

COSTAIN 0.16 0.57 0 0.13 0.15 6.66 5.79

COSTAIN 0.16 2.21 0 0.17 0.20 6.36 5.55

COSTAIN 0.16 0.68 0 0.08 0.09 6.16 5.03

COSTAIN 0.16 9.31 0 1.08 1.28 6.14 6.17

COSTAIN 0.16 1.76 0 0.98 1.16 6.18 #VALUE!

COSTAIN 0.16 2.93 0 0.07 0.08 6.06 5.67

COSTAIN 0.16 0.27 0 0.06 0.07 5.83 4.63

COSTAIN 0.16 0.13 0 0.05 0.05 5.70 4.30

COSTAIN 0.16 0 0 0.06 0.07 5.77 4.41

COSTAIN 0.16 0 0 0.39 0.46 5.77 5.36

COSTAIN 0.16 0 0 0.36 0.43 5.91 5.46

COSTAIN 0.16 0 0 0.02 0.03 5.66 3.51

COSTAIN 0.16 20 0 0.16 0.19 5.53 4.66

COSTAIN 0.16 9.15 0 0.13 0.16 5.51 4.55

G. CAPPA 0.16 0.41 0 0.01 0.01 6.94 4.71

G. CAPPA 0.16 1.14 0 0.07 0.08 6.30 5.15

G. CAPPA 0.16 0.14 0 0.01 0.01 6.33 4.25

G. CAPPA 0.16 1.97 0 0.11 0.13 6.34 5.39

G. CAPPA 0.16 2.62 0 0.15 0.17 6.35 5.52

G. CAPPA 0.16 0 0 0.20 0.23 6.36 5.41

G. CAPPA 0.16 0 0 0.18 0.22 6.39 5.43

G. CAPPA 0.16 0 0 0.23 0.27 6.39 5.54

141

G. CAPPA 0.16 0 0 0.31 0.37 6.39 5.76

G. CAPPA 0.16 0 0 0.11 0.13 6.34 5.09

G. CAPPA 0.16 0 0 0.24 0.28 5.76 5.14

G. CAPPA 0.16 0.39 0.32 1.22 0.10 0.11 5.72 4.70

G. CAPPA 0.16 1.04 0.6 1.73 0.60 0.72 5.48 5.26

G. CAPPA 0.16 0.9 0.4 2.25 0.65 0.77 5.38 5.16

G. CAPPA 0.16 0.7 0.24 2.92 0.56 0.67 5.27 4.93

ROADS NIGERIA 0.16 3.66 0.5 7.32 0.10 0.12 6.07 4.86

ROADS NIGERIA 0.16 4.01 0.5 8.02 0.01 0.01 6.08 3.91

ROADS NIGERIA 0.16 2.99 0.45 6.64 0.07 0.08 6.12 4.78

ROADS NIGERIA 0.16 2.07 0.4 5.18 0.17 0.20 5.64 4.62

ROADS NIGERIA 0.16 1.26 0.3 4.2 0.18 0.22 5.26 4.40

ROADS NIGERIA 0.16 0.18 0 0.08 0.09 5.17 3.99

ROADS NIGERIA 0.16 0.24 0 0.01 0.01 5.15 3.68

ROADS NIGERIA 0.16 0.7 0 0.04 0.05 5.32 3.77

ROADS NIGERIA 0.16 0.99 0 0.05 0.06 5.44 3.99

ROADS NIGERIA 0.16 0 0 0.03 0.03 5.65 3.95

ROADS NIGERIA 0.16 0 0 0.03 0.03 5.65 3.89

ROADS NIGERIA 0.16 0 0 0.02 0.02 5.70 3.60

ROADS NIGERIA 0.16 0.38 0.15 2.53 0.02 0.03 5.80 3.88

ROADS NIGERIA 0.16 0.41 0.1 4.1 0.02 0.02 5.71 3.91

ROADS NIGERIA 0.16 0.04 0 0.05 0.06 4.36 2.89

ROADS NIGERIA 0.16 0 0 0.31 0.37 4.18 3.58

UACN PROPERTY 0.16 1.69 0.55 3.07 0.05 0.06 7.69 6.36

UACN PROPERTY 0.16 2.21 0.5 4.42 0.05 0.06 7.72 6.38

UACN PROPERTY 0.16 3.35 0.75 4.47 0.07 0.08 7.73 6.57

UACN PROPERTY 0.16 0.39 0.49 0.8 0.01 0.02 7.75 5.63

UACN PROPERTY 0.16 0.88 0.35 2.51 0.04 0.04 7.56 5.98

UACN PROPERTY 0.16 0.77 0.25 3.08 0.04 0.04 7.44 5.92

UACN PROPERTY 0.16 0.45 0.2 2.25 0.03 0.03 7.39 5.66

UACN PROPERTY 0.16 0.91 0.45 2.02 0.05 0.06 7.30 5.96

UACN PROPERTY 0.16 0.74 0.35 2.11 0.05 0.05 7.27 5.87

UACN PROPERTY 0.16 0 0 0.05 0.06 7.22 5.83

UACN PROPERTY 0.16 0.48 0.3 1.6 0.07 0.08 6.95 5.69

UACN PROPERTY 0.16 0.15 0.14 1.07 0.02 0.03 6.96 5.19

UACN PROPERTY 0.16 0.13 0.12 1.08 0.02 0.03 6.85 5.12

DN TYRE & RUBBER PLC

0.16 0.09 0 0.15 0.18 6.79 5.59

142

DN TYRE & RUBBER PLC

0.16 2.33 0 0.74 0.88 6.81 7.05

DN TYRE & RUBBER PLC

0.16 0.44 0 0.10 0.12 7.31 6.32

DN TYRE & RUBBER PLC

0.16 0.86 0 0.03 0.04 7.30 5.81

DN TYRE & RUBBER PLC

0.16 0.33 0 0.01 0.01 7.23 5.39

CHAMPIONS BREWERIES

0.16 1.37 0 0.32 0.38 6.43 6.09

CHAMPIONS BREWERIES

0.16 1.13 0 0.31 0.37 6.51 5.96

CHAMPIONS BREWERIES

0.16 0.95 0 0.23 0.27 6.58 5.81

CHAMPIONS BREWERIES

0.16 0.37 0 0.08 0.09 6.64 5.36

CHAMPIONS BREWERIES

0.16 0.47 0 0.11 0.13 6.59 5.51

CHAMPIONS BREWERIES

0.16 0.11 0 0.03 0.04 6.48 4.89

CHAMPIONS BREWERIES

0.16 0.16 0 0.19 0.22 5.95 5.15

CHAMPIONS BREWERIES

0.16 0.12 0 0.14 0.16 5.89 4.98

CHAMPIONS BREWERIES

0.16 0.07 0 0.12 0.14 5.74 4.73

CHAMPIONS BREWERIES

0.16 0.14 0 0.25 0.29 5.70 5.03

GOLDEN BREWERIES 0.16 2.27 0 0.19 0.23 6.47 5.66

GOLDEN BREWERIES 0.16 1.55 0 0.13 0.15 6.49 5.46

GOLDEN BREWERIES 0.16 0.03 0 0.01 0.01 6.13 3.88

GOLDEN BREWERIES 0.16 85.36 0 0.07 0.08 6.23 4.98

GOLDEN BREWERIES 0.16 141.57 0 0.11 0.13 6.23 5.21

GOLDEN BREWERIES 0.16 0 0 0.10 0.12 6.28 5.19

GOLDEN BREWERIES 0.16 2.27 0 0.19 0.23 6.47 5.76

GOLDEN BREWERIES 0.16 1.55 0 0.13 0.15 6.49 5.59

GOLDEN BREWERIES 0.16 0.03 0 0.01 0.01 6.13 3.88

GOLDEN BREWERIES 0.16 85.36 0 0.07 0.08 6.23 5.07

GOLDEN BREWERIES 0.16 141.57 0 0.01 0.01 5.59 3.28

GOLDEN BREWERIES 0.16 14.16 0 0.48 0.57 5.59 5.21

GOLDEN BREWERIES 0.16 7.62 0 0.03 0.03 5.54 4.02

GOLDEN BREWERIES 0.16 5.08 0 0.04 0.04 5.41 3.84

GOLDEN BREWERIES 0.16 33.78 25 1.35 0.34 0.40 5.23 4.66

A.G LEVENTIS 0.16 0.29 0 0.06 0.07 7.10 5.81

A.G LEVENTIS 0.16 0.4 0 0.14 0.16 7.11 6.09

A.G LEVENTIS 0.16 0.36 0 0.16 0.19 7.04 6.09

A.G LEVENTIS 0.16 0.3 0 0.15 0.18 6.84 5.88

A.G LEVENTIS 0.16 0.18 0 0.19 0.22 6.57 5.67

A.G LEVENTIS 0.16 0.11 0.08 1.38 0.16 0.19 6.58 5.58

143

A.G LEVENTIS 0.16 0.01 0.07 0.14 0.01 0.01 7.56 5.38

A.G LEVENTIS 0.16 0.13 0.07 1.86 0.03 0.03 7.06 5.27

A.G LEVENTIS 0.16 0.08 0.07 1.14 0.12 0.14 6.06 4.77

A.G LEVENTIS 0.16 0.04 0.06 0.67 0.03 0.04 6.38 4.56

A.G LEVENTIS 0.16 0.07 0.05 1.4 0.02 0.02 6.32 4.03

A.G LEVENTIS 0.16 0.23 0.1 2.3 0.05 0.06 6.29 4.85

A.G LEVENTIS 0.17 0.3 0 0.00 0.00 6.20 4.01

A.G LEVENTIS 0.19 0.31 0.12 2.58 0.07 0.09 6.14 4.95

CHELLARAMS PLC 0.16 0.3 0.1 3 0.08 0.09 6.63 5.34

CHELLARAMS PLC 0.16 0.61 0.08 7.63 0.10 0.12 6.60 5.65

CHELLARAMS PLC 0.16 0.73 0 0.03 0.03 6.56 5.58

CHELLARAMS PLC 0.16 0.68 0.15 4.53 0.09 0.10 6.56 5.41

CHELLARAMS PLC 0.16 0.74 1.5 0.49 0.08 0.09 6.45 5.44

CHELLARAMS PLC 0.16 2.79 1.5 1.86 0.04 0.05 6.40 4.51

CHELLARAMS PLC 0.16 3.47 1 3.47 0.05 0.06 6.24 4.75

CHELLARAMS PLC 0.16 2.39 1 2.39 0.06 0.07 6.09 4.63

CHELLARAMS PLC 0.16 2.6 0.1 26 0.07 0.09 5.81 4.50

CHELLARAMS PLC 0.16 0.2 0.07 2.86 0.60 0.71 5.81 4.38

CHELLARAMS PLC 0.16 0.21 0.07 3 0.05 0.06 5.77 4.41

CHELLARAMS PLC 0.16 0.24 0.07 3.43 0.07 0.08 5.70 4.47

CHELLARAMS PLC 0.16 0.15 0.05 3 0.05 0.07 5.63 4.27

CHELLARAMS PLC 0.16 0.12 0.03 4 0.08 0.09 5.41 4.14

JOHN HOLTS 0.16 2.63 0 0.00 0.00 7.01 4.00

JOHN HOLTS 0.16 551.16 0 0.00 0.00 6.96 6.33

JOHN HOLTS 0.16 100.26 0 0.01 0.01 6.83 5.59

JOHN HOLTS 0.16 9.74 0 0.02 0.02 6.73 4.58

JOHN HOLTS 0.16 122.05 0 0.08 0.10 6.66 5.68

JOHN HOLTS 0.16 6.43 0 0.00 0.00 6.66 4.40

JOHN HOLTS 0.16 17.98 1 17.98 0.07 0.08 6.55 4.85

JOHN HOLTS 0.16 56.28 0 0.03 0.03 6.66 5.34

JOHN HOLTS 0.16 0.45 0 0.08 0.10 6.53 5.25

JOHN HOLTS 0.16 3.39 0 0.10 0.11 6.43 5.13

JOHN HOLTS 0.16 20.04 0 0.02 0.03 6.41 4.88

JOHN HOLTS 0.16 455.86 0 0.60 0.71 6.44 6.25

JOHN HOLTS 0.16 17.48 15 1.17 0.05 0.06 6.46 4.85

JOHN HOLTS 0.16 67.07 35 1.92 0.16 0.20 6.32 5.42

SCOA NIGERIA PLC 0.16 0.33 0.08 4.13 0.22 0.26 6.00 5.33

144

SCOA NIGERIA PLC 0.16 1.1 0.1 11 0.78 0.92 6.02 5.85

SCOA NIGERIA PLC 0.16 0.36 0.1 3.6 0.22 0.26 6.11 5.37

SCOA NIGERIA PLC 0.16 1.26 0.15 8.4 0.90 1.07 6.05 5.91

SCOA NIGERIA PLC 0.16 1.09 0.1 10.9 1.40 1.66 5.77 5.85

SCOA NIGERIA PLC 0.16 13.35 0 2.99 3.55 5.46 5.94

SCOA NIGERIA PLC 0.16 5.03 0 1.03 1.22 5.53 5.51

SCOA NIGERIA PLC 0.16 0.85 0 0.22 0.26 5.48 4.62

SCOA NIGERIA PLC 0.16 2.11 0.15 14.07 0.31 0.36 5.57 5.02

SCOA NIGERIA PLC 0.48 3.59 0.15 23.93 0.78 0.92 5.53 5.25

SCOA NIGERIA PLC 0.51 3.37 0.15 22.47 0.79 0.93 5.43 5.22

SCOA NIGERIA PLC 0.35 1.78 0.1 17.8 0.31 0.37 5.57 4.94

SCOA NIGERIA PLC 0.28 0.69 0.05 13.8 0.11 0.13 5.63 4.53

SCOA NIGERIA PLC 0.30 1.18 0.07 16.86 0.13 0.15 5.78 4.76

TRANSCORP 0.32 0.21 0 0.18 0.26 7.60 6.73

TRANSCORP 0.30 0.05 0 0.10 0.14 7.52 6.09

TRANSCORP 0.64 0.23 0 0.03 0.09 8.06 6.71

TRANSCORP 0.65 199.78 0 0.06 0.17 8.07 6.90

TRANSCORP 0.67 0 0 0.07 0.21 8.07 6.97

DANGOTE CEMENT 0.45 0.07 0 0.30 0.36 8.52 8.03

DANGOTE CEMENT 0.36 1.23 0 0.26 0.31 8.38 7.79

DANGOTE CEMENT 0.42 0.95 0 0.27 0.32 8.27 7.67

DANGOTE CEMENT 0.48 0.36 0 0.15 0.18 8.25 7.25

DANGOTE CEMENT 0.61 0.23 0 0.07 0.09 8.23 7.07

DN MEYER PLC. 0.40 0.73 0 0.09 0.14 6.39 5.37

DN MEYER PLC. 0.32 1.93 0 0.18 0.27 6.42 5.80

DN MEYER PLC. 0.17 1.02 0.1 10.2 0.11 0.13 6.43 5.47

DN MEYER PLC. 0.06 2.12 0 0.08 0.09 6.00 4.80

DN MEYER PLC. 0.58 0.25 0 0.14 0.16 5.53 4.78

DN MEYER PLC. 0.14 0.86 0 0.63 0.73 5.51 5.32

DN MEYER PLC. 0.14 0.32 0.2 1.6 0.25 0.29 5.55 4.80

DN MEYER PLC. 0.14 0.46 0.45 1.02 0.33 0.38 5.56 4.82

DN MEYER PLC. 0.14 0.52 0.5 1.04 0.31 0.36 5.55 4.88

DN MEYER PLC. 0.14 0.49 0.4 1.23 0.51 0.60 5.30 4.86

DN MEYER PLC. 0.14 0.53 0.3 1.77 0.29 0.34 5.29 4.66

DN MEYER PLC. 0.14 1.27 0.2 6.35 0.22 0.25 5.28 4.59

DN MEYER PLC. 0.14 0.27 0.2 1.35 0.46 0.54 5.33 4.97

DN MEYER PLC. 0.21 0 0 0.12 0.14 5.29 4.29

145

FIRST ALUMINUM NIGERIA PLC

0.18 1.59 0 0.04 0.04 6.91 5.52

FIRST ALUMINUM NIGERIA PLC

0.17 0.23 0 0.01 0.01 6.91 4.68

FIRST ALUMINUM NIGERIA PLC

0.21 2.32 0 0.17 0.19 6.45 5.48

FIRST ALUMINUM NIGERIA PLC

0.64 3.81 0 0.20 0.23 6.48 5.69

FIRST ALUMINUM NIGERIA PLC

0.28 11.86 0 0.10 0.12 6.49 3.74

FIRST ALUMINUM NIGERIA PLC

0.21 12.31 0 0.08 0.09 6.39 5.20

FIRST ALUMINUM NIGERIA PLC

0.26 11.53 0 0.05 0.06 6.38 4.97

FIRST ALUMINUM NIGERIA PLC

0.14 11.79 0 0.12 0.14 6.28 5.28

FIRST ALUMINUM NIGERIA PLC

0.20 4.69 0 0.14 0.16 6.30 5.34

FIRST ALUMINUM NIGERIA PLC

0.30 5.02 0 0.09 0.10 6.31 5.20

FIRST ALUMINUM NIGERIA PLC

0.17 1.94 1.13 1.72 0.07 0.09 6.09 4.79

FIRST ALUMINUM NIGERIA PLC

0.15 3.68 16.21 0.23 0.19 0.22 5.97 5.07

FIRST ALUMINUM NIGERIA PLC

0.19 2.5 1.15 2.17 0.16 0.19 5.94 4.90

FIRST ALUMINUM NIGERIA PLC

0.22 2.62 1.15 2.28 0.14 0.16 5.93 4.92

IPWA PLC 0.14 11.61 0 0.33 0.39 5.37 4.78

IPWA PLC 0.14 0.86 0 0.38 0.44 4.45 3.65

IPWA PLC 0.14 4.18 0 0.09 0.11 5.50 4.33

IPWA PLC 0.14 13.36 1.16 11.52 0.23 0.27 5.50 4.84

IPWA PLC 0.55 13.99 0 0.15 0.34 5.51 4.70

IPWA PLC 0.00 12.3 0 0.00 0.00 5.70 4.53

IPWA PLC 0.46 26.38 0 0.06 0.12 5.73 4.54

IPWA PLC 0.49 26.08 0 0.13 0.26 5.76 4.89

IPWA PLC 0.59 37.39 0 0.18 0.44 5.79 5.06

IPWA PLC 0.17 0 0 0.17 0.20 5.86 #NUM!

IPWA PLC 57.31 167.39 0 0.59 -0.01 5.38 5.15

IPWA PLC 0.17 47.62 0 0.17 0.20 5.37 4.60

IPWA PLC 0.02 12.97 12.5 1.04 0.06 0.06 5.38 4.04

IPWA PLC 0.02 10.88 10 1.09 0.05 0.05 5.38 3.96

IPWA PLC 0.04 11.66 0 0.05 0.05 5.39 3.99

LAFARGE CEMENT WAPCO NIGERIA PLC

0.31 1.63 0 0.06 0.09 8.12 7.69

LAFARGE CEMENT WAPCO NIGERIA PLC

0.16 1.68 0.1 16.8 0.00 0.00 7.96 6.70

LAFARGE CEMENT WAPCO NIGERIA PLC

0.16 3.75 0.6 6.25 0.23 0.28 7.75 7.05

LAFARGE CEMENT WAPCO NIGERIA PLC

0.16 0.35 1.2 0.29 0.29 0.34 7.64 #VALUE!

LAFARGE CEMENT WAPCO NIGERIA PLC

0.00 3.65 1 3.65 0.29 0.29 7.62 7.04

PAINTS & COATING 3.11 0.13 0 0.54 -0.26 5.30 5.03

146

MANUFACTURES NIGERIA PLC PAINTS & COATING MANUFACTURES NIGERIA PLC

0.90 0.02 0 0.14 1.44 5.32 4.25

PAINTS & COATING MANUFACTURES NIGERIA PLC

43.07 0.15 0.05 3 17.22 -0.41 3.80 4.88

PAINTS & COATING MANUFACTURES NIGERIA PLC

52.01 4.52 2.3 1.97 9.99 -0.20 3.84 4.66

PAINTS & COATING MANUFACTURES NIGERIA PLC

17.73 1.7 1.7 1 3.05 -0.22 4.23 4.53

VITAFOAM 1.67 0.63 0.3 2.1 0.37 -0.89 6.34 5.71

VITAFOAM 1.27 0.63 0.25 2.52 0.36 -1.33 6.33 5.71

VITAFOAM 1.20 0.85 0.3 2.83 0.58 -2.85 6.24 5.84

VITAFOAM 2.10 0.54 0.25 2.16 0.80 -0.72 5.91 5.64

VITAFOAM 0.15 0 0 0.55 0.66 6.01 5.66

VITAFOAM 1.26 17.8 15 1.19 0.27 -1.02 5.81 5.05

VITAFOAM 1.30 0.41 30 0.01 0.55 -1.84 5.86 5.43

VITAFOAM 1.98 47 30 1.57 0.63 -0.65 5.88 5.49

VITAFOAM 1.40 59 40 1.48 0.56 -1.42 5.86 5.41

VITAFOAM 1.42 59 40 1.48 0.67 -1.58 5.77 5.41

VITAFOAM 1.64 34.59 25 1.38 0.53 -0.83 5.66 5.18

VITAFOAM 0.75 46.22 30 1.54 0.60 1.74 5.53 5.13

VITAFOAM 0.00 36.34 25 1.45 0.75 0.75 5.36 5.02

VITAFOAM 2.11 70.01 40 1.75 1.08 -0.97 5.12 5.01

VONO PRODUCT 0.30 1.32 0 0.18 0.25 6.35 5.60

VONO PRODUCT 0.26 0.85 0 0.11 0.14 6.37 5.40

VONO PRODUCT 0.32 0.4 0 0.12 0.18 5.98 5.08

VONO PRODUCT 0.48 1.83 0 0.54 1.03 6.01 5.74

VONO PRODUCT 0.17 0.04 0 0.01 0.01 5.46 3.05

VONO PRODUCT 0.17 0.51 0 0.33 0.40 5.43 4.89

VONO PRODUCT 0.17 3.63 0.2 18.15 1.22 1.47 5.22 5.28

VONO PRODUCT 0.17 0.5 0.2 2.5 0.08 0.10 5.35 4.20

VONO PRODUCT 0.17 0.3 0.15 2 0.10 0.12 5.38 4.18

VONO PRODUCT 0.17 0.04 0.1 0.4 0.02 0.02 5.35 3.24

VONO PRODUCT 0.17 0.33 0 0.09 0.11 5.23 4.08

VONO PRODUCT 0.17 2.48 0 0.05 0.06 5.37 3.95

VONO PRODUCT 0.17 174.2 0 0.27 0.32 5.36 4.80

VONO PRODUCT 0.17 3.32 0.3 11.07 0.06 0.08 5.39 4.08

PZ CUSSONS 0.17 1.67 0.86 1.94 0.25 0.30 7.51 6.72

147

PZ CUSSONS 0.17 1.52 0.68 2.24 0.27 0.33 7.45 6.68

PZ CUSSONS 0.17 1.24 0.62 2 0.25 0.30 7.37 6.60

PZ CUSSONS 0.17 1.38 0.71 1.94 0.25 0.30 7.33 6.55

PZ CUSSONS 0.17 1.45 0.69 2.1 0.26 0.31 7.27 6.51

PZ CUSSONS 0.17 1.49 0.75 1.99 0.28 0.34 7.19 6.51

PZ CUSSONS 0.17 1.19 0.75 1.59 0.21 0.26 7.19 6.52

PZ CUSSONS 0.17 1.28 0.66 1.94 0.26 0.32 7.04 6.30

PZ CUSSONS 0.17 0.97 0.47 2.06 0.26 0.31 6.97 6.23

PZ CUSSONS 0.17 0.87 0.45 1.93 0.21 0.25 6.93 6.10

PZ CUSSONS 0.17 0.64 0.4 1.6 0.15 0.19 6.94 5.97

PZ CUSSONS 0.17 0.59 0.31 1.9 0.02 0.02 6.95 5.93

PZ CUSSONS 0.17 0.79 0.27 2.93 0.09 0.11 7.25 6.06

PZ CUSSONS 0.17 1.03 0.27 3.81 0.53 0.64 6.61 6.17

UNILEVER 0.22 1.11 0 0.40 0.41 7.18 6.62

UNILEVER 0.39 1.08 0 0.44 0.52 7.11 6.61

UNILEVER 0.42 0.69 0 0.35 0.42 7.07 6.41

UNILEVER 0.32 0.28 0 0.18 0.20 7.05 6.11

UNILEVER 0.38 0.43 0 0.21 0.24 7.00 6.14

UNILEVER 0.46 0.53 0 0.23 0.28 7.00 6.21

UNILEVER 0.43 0.72 0.7 1.03 0.37 0.44 6.90 6.34

UNILEVER 0.44 0.62 0.61 1.02 0.44 0.53 6.80 6.27

UNILEVER 0.38 0.52 0.05 10.4 0.35 0.42 6.77 6.20

UNILEVER 0.38 0.72 0.42 1.71 0.34 0.41 6.67 6.34

UNILEVER 0.17 0.71 0.7 1.01 0.34 0.41 6.58 5.93

UNILEVER 0.17 0.36 0.35 1.03 0.17 0.21 6.53 5.64

UNILEVER 0.17 0.11 0.11 1 0.09 0.11 6.50 5.14

UNILEVER 0.17 0.13 0 0.03 0.03 6.53 5.12

EKOCORP PLC 0.17 0.06 0 0.03 0.03 9.19 7.47

EKOCORP PLC 0.17 0.06 0 0.03 0.03 9.21 7.50

EKOCORP PLC 0.17 0.12 0 0.05 0.06 9.17 7.76

EKOCORP PLC 0.12 0.19 0.15 1.27 0.06 0.07 9.14 7.86

EKOCORP PLC 0.19 0.2 0.15 1.33 0.05 0.07 9.18 7.82

EKOCORP PLC 0.13 0.19 0 0.10 0.12 8.86 7.79

EKOCORP PLC 0.13 0.22 0.05 4.4 0.11 0.13 8.76 7.76

EKOCORP PLC 0.13 0.26 0 0.10 0.12 8.77 7.74

EKOCORP PLC 0.13 0.3 0 0.14 0.16 8.63 7.71

EKOCORP PLC 0.13 0.27 0.13 2.08 0.12 0.14 8.64 7.67

148

EKOCORP PLC 0.13 0 0 0.10 0.11 8.77 7.67

EKOCORP PLC 0.13 0 0 0.06 0.07 8.85 7.56

EKOCORP PLC 0.13 0 0 0.09 0.11 8.65 7.54

EKOCORP PLC 0.13 0 0 0.09 0.10 8.60 7.47

EKOCORP PLC 0.13 0 0 0.29 0.33 8.24 7.59

EKOCORP PLC 0.13 0 0 0.28 0.32 8.21 7.51

UNION DIAGONISTIC AND CLINICAL SERVICES

0.13 0.04 0 0.11 0.12 6.29 5.20

UNION DIAGONISTIC AND CLINICAL SERVICES

0.13 0.03 0 0.08 0.09 6.37 5.04

UNION DIAGONISTIC AND CLINICAL SERVICES

0.13 0.08 0 0.31 0.36 6.36 5.57

UNION DIAGONISTIC AND CLINICAL SERVICES

0.13 0.06 0 1.38 1.58 5.36 5.41

UNION DIAGONISTIC AND CLINICAL SERVICES

0.13 0.01 0 0.24 0.28 5.42 4.77

EVANS MEDICAL 1.98 0.13 0 0.03 -0.18 6.32 3.94

EVANS MEDICAL 1.84 0.15 0 0.30 -0.42 6.34 5.95

EVANS MEDICAL 1.56 0.66 0 0.17 -0.31 6.35 5.71

EVANS MEDICAL 1.46 2.79 0 0.18 -0.50 6.32 5.50

EVANS MEDICAL 1.19 3.71 0 0.10 -0.94 6.26 5.12

EVANS MEDICAL 0.10 0 0 0.11 0.12 6.24 5.19

EVANS MEDICAL 0.10 0.14 0 0.04 0.04 6.16 4.10

EVANS MEDICAL 0.10 0.25 0.2 1.25 0.03 0.03 6.14 4.95

EVANS MEDICAL 0.00 0.83 1.25 0.66 0.02 0.02 6.15 4.99

EVANS MEDICAL 0.09 0.51 1 0.51 0.02 0.02 6.16 4.78

EVANS MEDICAL 0.13 0.44 0.75 0.59 0.04 0.04 6.18 4.68

EVANS MEDICAL 1.17 0.51 0 0.31 -1.80 5.33 4.78

EVANS MEDICAL 0.88 2.05 0 1.03 8.94 5.37 5.38

EVANS MEDICAL 0.17 0.08 0 0.01 0.01 5.36 3.96

EVANS MEDICAL 0.56 0.26 0 0.22 0.48 5.24 4.48

MORRISON INDUSTRIES

0.26 0.22 0 0.07 0.10 5.68 4.52

MORRISON INDUSTRIES

0.25 0.14 0 0.04 0.05 5.71 4.32

MORRISON INDUSTRIES

0.18 0.09 0 0.04 0.05 5.73 4.16

MORRISON INDUSTRIES

0.93 0.04 0 0.01 0.09 4.95 3.74

MORRISON INDUSTRIES

0.88 0.09 0 0.17 1.35 4.94 3.91

MORRISON INDUSTRIES

0.08 0 0 0.19 0.20 4.86 4.52

MORRISON INDUSTRIES

1.10 10.59 10 1.06 0.40 -4.20 4.69 3.99

149

MORRISON INDUSTRIES

0.64 10.43 0.75 13.91 0.37 1.01 4.64 3.98

MORRISON INDUSTRIES

0.55 6.94 6 1.16 0.24 0.53 4.66 3.80

MORRISON INDUSTRIES

0.53 12.69 5 2.54 0.12 0.25 4.73 4.06

MORRISON INDUSTRIES

0.21 4.36 0 0.06 0.07 4.76 3.50

MORRISON INDUSTRIES

0.25 19.12 0 0.19 0.25 4.69 3.85

MORRISON INDUSTRIES

0.57 24.15 20 1.21 0.22 0.51 4.55 3.89

MORRISON INDUSTRIES

1.60 30.45 25 1.22 0.58 -0.97 4.23 3.99

FIDSON HEALTH CARE

0.23 0.31 0.1 3.1 0.17 0.22 6.45 5.67

FIDSON HEALTH CARE

0.25 0.29 0.22 1.32 0.17 0.23 6.39 5.63

FIDSON HEALTH CARE

0.47 0.13 0.2 0.65 0.17 0.31 6.05 5.28

FIDSON HEALTH CARE

0.66 4.02 2.27 1.77 0.66 1.97 5.88 5.70

FIDSON HEALTH CARE

0.57 3.05 1.1 2.77 0.57 1.32 5.81 5.57

PHARMA DECO 2.31 4.66 0 0.62 -1.71 5.87 5.67

PHARMA DECO 1.71 4.64 0 0.61 -0.85 5.88 5.66

PHARMA DECO 1.13 2.08 0 0.24 -1.83 5.91 5.30

PHARMA DECO 0.93 3.55 0 0.28 3.96 5.93 5.38

PHARMA DECO 0.73 93.97 0 0.45 1.67 5.90 5.53

PHARMA DECO 0.02 0.09 0 0.02 0.02 5.81 3.91

PHARMA DECO 0.08 0.36 0.2 1.8 0.08 0.08 5.68 4.49

PHARMA DECO 0.21 0.82 0.2 4.1 0.25 0.32 5.44 4.80

PHARMA DECO 5.54 1.06 0.1 10.6 0.24 -0.05 5.32 4.63

PHARMA DECO 5.54 0.12 0 0.03 -0.01 5.34 3.68

PHARMA DECO 5.54 0 0 0.90 -0.20 5.34 4.82

PHARMA DECO 0.17 0 0 0.44 0.51 5.33 4.98

PHARMA DECO 0.12 0 0 0.16 0.17 5.35 4.56

PHARMA DECO 0.15 36.53 35 1.04 0.07 0.07 5.34 4.16

PHARMA DECO 1.23 29.59 25 1.18 0.05 0.06 5.35 4.07

PHARMA DECO 3.21 62.86 20 3.14 1.23 1.34 4.31 4.40

ASHAKA CEMENT 0.08 1.51 0.3 5.03 0.18 0.20 7.39 6.60

ASHAKA CEMENT 0.12 0.47 0 0.10 0.11 7.39 5.97

ASHAKA CEMENT 0.00 1.21 0 0.16 0.16 7.33 6.32

ASHAKA CEMENT 0.03 1.1 1.5 0.73 0.15 0.16 7.22 6.21

ASHAKA CEMENT 0.06 2.31 0 #DIV/0! 0.47 0.51 7.02 6.53

ASHAKA CEMENT 0.06 3.03 2.32 1.31 1.11 1.18 6.77 6.65

ASHAKA CEMENT 0.06 3.85 2.88 1.34 1.51 1.60 6.51 6.53

150

ASHAKA CEMENT 0.06 2.42 17.09 0.14 1.29 1.37 6.39 6.33

ASHAKA CEMENT 0.06 1.74 6 0.29 1.05 1.12 6.30 6.18

ASHAKA CEMENT 0.06 31.64 0.75 42.19 1.42 1.51 6.29 6.27

ASHAKA CEMENT 0.06 14.74 0.6 24.57 0.89 0.95 6.18 5.94

ASHAKA CEMENT 0.06 9.07 0.3 30.23 0.78 0.83 6.05 5.75

ASHAKA CEMENT 0.06 6.7 0.2 33.5 0.53 0.57 6.02 5.60

ASHAKA CEMENT 0.06 10.76 0.2 53.8 0.87 0.92 6.02 5.80

AFRICAN PAINT 0.02 0 0 0.03 0.03 5.66 4.16

AFRICAN PAINT 0.04 0 0 0.07 0.07 5.67 4.50

AFRICAN PAINT 0.02 0 0 0.13 0.13 5.68 4.79

AFRICAN PAINT 0.06 0 0 0.05 0.05 5.56 4.22

AFRICAN PAINT 0.12 0 0 0.06 0.06 5.58 4.35

BERGER PAINTS 1.33 2.03 0 0.38 -1.16 6.14 5.65

BERGER PAINTS 1.12 0.89 0 0.23 -1.92 6.14 5.29

BERGER PAINTS 0.99 0.95 0 0.17 17.68 6.15 5.17

BERGER PAINTS 754.09 0.52 0 126.36 -0.17 3.22 5.05

BERGER PAINTS 0.70 0.37 0 0.07 0.23 6.21 4.91

BERGER PAINTS 0.63 20.61 0 -0.04 -0.11 6.22 #NUM!

BERGER PAINTS 0.36 46.49 0 0.10 0.16 6.22 5.01

BERGER PAINTS 1.85 49.76 0 0.55 -0.65 5.49 5.04

BERGER PAINTS 1.60 39.5 0 0.40 -0.67 5.51 4.93

BERGER PAINTS 1.68 40.66 30 1.36 0.49 -0.72 5.44 4.95

BERGER PAINTS 1.78 10.54 10 1.05 0.16 -0.21 5.37 4.30

BERGER PAINTS 2.23 18.37 30 0.61 0.29 -0.24 5.27 4.54

BERGER PAINTS 1.17 36.21 40 0.91 0.28 -1.60 5.28 1.60

BERGER PAINTS 2.41 67.39 40 1.68 1.03 -0.73 4.96 4.87

CHEMICAL AND ALLIED

0.05 3.15 3 1.05 3.53 3.73 5.51 5.95

CHEMICAL AND ALLIED

0.05 1.62 1.6 1.01 1.94 2.05 5.50 5.53

CHEMICAL AND ALLIED

0.05 3.5 3.3 1.06 3.24 3.42 5.49 5.87

CHEMICAL AND ALLIED

0.05 1.67 3.75 0.45 3.01 3.18 5.27 5.55

CHEMICAL AND ALLIED

0.05 1.49 3 0.5 2.04 2.15 5.35 5.50

CHEMICAL AND ALLIED

0.05 0 0 1.44 1.53 5.32 5.30

CHEMICAL AND ALLIED

0.05 0 0 1.09 1.15 5.36 5.21

CHEMICAL AND ALLIED

0.05 0 0 1.10 1.16 5.28 5.18

CHEMICAL AND ALLIED

0.05 0 0 1.08 1.14 5.22 5.15

CHEMICAL AND ALLIED

0.05 3.18 0.25 12.72 4.44 4.69 4.97 5.60

151

CHEMICAL AND ALLIED

0.05 0.07 0 0.19 0.20 4.91 3.94

CHEMICAL AND ALLIED

0.09 0.75 0 1.36 1.50 5.03 4.98

CHEMICAL AND ALLIED

0.05 0.17 0 0.25 0.27 5.08 4.24

CHEMICAL AND ALLIED

0.05 0.67 0 0.09 0.09 5.14 4.85

CEMENT COMPANY OF NORTHERN NIGERIA

0.26 1.01 0 0.00 0.00 8.57 8.03

CEMENT COMPANY OF NORTHERN NIGERIA

0.20 1.84 0.9 2.04 0.01 0.01 8.38 7.79

CEMENT COMPANY OF NORTHERN NIGERIA

0.30 1.34 0.9 1.49 0.01 0.01 8.27 7.67

CEMENT COMPANY OF NORTHERN NIGERIA

0.36 0.11 0.1 1.1 0.00 0.00 8.25 7.25

CEMENT COMPANY OF NORTHERN NIGERIA

0.48 0.32 0 0.00 0.00 8.23 7.07

CEMENT COMPANY OF NORTHERN NIGERIA

1.26 2.07 1 2.07 0.14 -0.52 6.44 4.35

CEMENT COMPANY OF NORTHERN NIGERIA

0.94 85.12 0 0.30 5.29 6.45 5.92

CEMENT COMPANY OF NORTHERN NIGERIA

0.77 1.47 0 0.03 0.15 6.43 5.03

CEMENT COMPANY OF NORTHERN NIGERIA

0.96 14.66 0 0.41 10.92 6.14 5.75

CEMENT COMPANY OF NORTHERN NIGERIA

0.49 13.36 0 0.89 1.76 6.08 6.03

CEMENT COMPANY OF NORTHERN NIGERIA

0.52 0.21 0 0.59 1.24 5.92 5.71

CEMENT COMPANY OF NORTHERN NIGERIA

0.46 0.38 0 0.01 0.03 5.94 3.81

CEMENT COMPANY OF NORTHERN NIGERIA

0.35 0.47 0 0.01 0.02 5.96 3.84

CEMENT COMPANY OF NORTHERN NIGERIA

0.05 0.45 0 0.02 0.02 5.97 3.54

UTC NIGERIA 0.17 0.06 0 0.00 0.00 6.42 4.90

UTC NIGERIA 0.15 0.06 0 0.03 0.03 6.41 4.87

UTC NIGERIA 0.21 0.08 0 0.02 0.02 6.42 4.67

UTC NIGERIA 0.17 0.03 0 0.02 0.02 6.39 4.57

UTC NIGERIA 0.11 0.05 0 0.05 0.05 6.06 4.72

UTC NIGERIA 0.07 0.07 0 0.50 0.53 5.76 5.22

UTC NIGERIA 0.07 0.07 0 0.17 0.18 6.23 4.87

UTC NIGERIA 0.07 0.16 0 0.11 0.12 6.45 5.54

UTC NIGERIA 0.07 0.14 0 0.11 0.12 6.44 5.57

152

UTC NIGERIA 0.37 0.4 0 0.05 0.07 6.53 5.07

UTC NIGERIA 0.47 0.14 0 0.02 0.04 6.51 4.74

UTC NIGERIA 1.91 1.62 0 0.40 -0.44 6.05 5.65

UTC NIGERIA 1.66 3.33 0 0.87 -1.32 6.03 5.97

UTC NIGERIA 0.65 2.2 0 0.41 1.16 6.17 5.79

UNION DICON SALT 0.05 0 0 0.15 0.16 6.18 5.34

UNION DICON SALT 0.05 0 0 0.10 0.10 6.31 5.28

UNION DICON SALT 0.05 0 0 0.09 0.09 6.37 5.28

UNION DICON SALT 8.12 0.87 0 2.01 -0.28 5.00 5.31

UNION DICON SALT 5.15 0.81 0 1.21 -0.29 5.19 5.28

UNION DICON SALT 3.29 0.61 0 0.59 -0.26 5.38 5.15

UNION DICON SALT 2.04 2.08 0 1.40 -1.34 5.54 5.68

UNION DICON SALT 1.75 2.34 0 1.36 -1.81 5.65 5.57

UNION DICON SALT 0.05 0 0 1.34 1.41 5.77 5.87

UNION DICON SALT 0.05 0 0 0.77 0.81 5.85 5.75

UNION DICON SALT 0.05 1.51 0 0.30 0.32 5.91 5.33

UNION DICON SALT 0.05 1.86 1.5 1.24 0.29 0.30 5.99 5.42

UNION DICON SALT 0.05 2.14 1.4 1.53 0.46 0.49 5.85 5.48

UNION DICON SALT 0.05 2.24 1.7 1.32 0.32 0.33 6.01 5.50

UNION DICON SALT 0.05 2.59 1.5 1.73 0.33 0.35 6.04 5.56

CADBURY NIGERIA 15.52 0.38 0 1.97 -0.17 6.00 6.07

CADBURY NIGERIA 11.60 0.84 0 21.93 -3.00 6.04 7.96

CADBURY NIGERIA 28.29 2.44 0 2.99 -0.13 5.98 6.44

CADBURY NIGERIA 1.51 0.66 0 5.87 -18.28 7.21 5.86

CADBURY NIGERIA 1.92 4.28 0 0.40 -0.59 7.15 6.67

CADBURY NIGERIA 0.08 2.7 1.3 2.08 0.39 0.42 7.00 6.43

CADBURY NIGERIA 0.08 2.81 1.6 1.76 0.48 0.52 6.91 6.45

CADBURY NIGERIA 0.08 3.57 1.75 2.04 0.46 0.50 6.92 5.37

CADBURY NIGERIA 0.08 3 1.8 1.67 0.75 0.82 6.64 6.35

CADBURY NIGERIA 0.08 2.06 1.2 1.72 0.82 0.89 6.47 6.22

CADBURY NIGERIA 0.08 2.02 1.1 1.84 0.57 0.62 6.46 6.03

CADBURY NIGERIA 0.08 1.51 1 1.51 0.48 0.52 6.41 5.88

CADBURY NIGERIA 0.08 1.41 0.73 1.93 0.38 0.41 6.43 5.86

CADBURY NIGERIA 0.08 1.34 0.67 2 0.40 0.43 6.37 5.85

NESTLE NIGERIA 0.88 19.08 0 0.35 0.56 7.72 7.10

NESTLE NIGERIA 1.02 14.81 0 0.42 0.98 7.52 6.99

NESTLE NIGERIA 1.12 12.61 0 0.66 1.73 7.25 6.92

153

NESTLE NIGERIA 1.11 8.89 0 0.62 1.59 7.13 6.74

NESTLE NIGERIA 1.32 1.07 1 1.07 0.86 3.71 6.98 6.75

NESTLE NIGERIA 1.36 1 0.7 1.43 0.99 9.90 6.91 6.72

NESTLE NIGERIA 1.74 7.26 7 1.04 1.18 -9.42 6.71 6.59

NESTLE NIGERIA 2.78 7.2 6 1.2 2.12 -2.25 6.44 6.58

NESTLE NIGERIA 3.28 7.51 6.5 1.16 2.94 -2.44 6.20 6.50

NESTLE NIGERIA 0.80 5.98 5.5 1.09 0.53 1.35 6.84 6.40

NESTLE NIGERIA 0.81 3.8 3.75 1.01 0.49 1.13 6.66 6.21

NESTLE NIGERIA 0.88 2.96 2 1.48 0.51 1.09 6.50 6.10

NESTLE NIGERIA 1.22 1.9 1.7 1.12 0.38 1.41 6.36 5.90

NESTLE NIGERIA 1.14 1.68 1.5 0.27 1.28 6.47 5.85

NIGERIA ENAMELWARE

0.10 1.1 0 2.12 2.36 4.72 4.87

NIGERIA ENAMELWARE

0.10 2.2 0 1.72 1.92 4.73 4.80

NIGERIA ENAMELWARE

0.10 0.69 0 3.86 4.31 4.03 4.30

NIGERIA ENAMELWARE

0.10 0.85 0.6 1.42 3.38 3.77 4.05 4.39

NIGERIA ENAMELWARE

0.10 0.22 0.5 0.44 2.34 2.61 4.13 3.80

NIGERIA ENAMELWARE

0.10 0.33 0.5 0.66 1.41 1.57 4.40 3.98

NIGERIA ENAMELWARE

0.10 0.55 0.4 1.38 0.61 0.68 4.64 4.20

NIGERIA ENAMELWARE

0.10 0.5 0.35 1.43 0.41 0.45 4.81 4.16

NIGERIA ENAMELWARE

0.10 0.55 0.3 1.83 0.31 0.35 4.90 4.20

NIGERIA ENAMELWARE

1.64 66.1 16 4.13 0.56 0.63 4.64 4.28

NIGERIA ENAMELWARE

2.84 34.57 14 2.47 1.20 1.34 4.38 4.00

NIGERIA ENAMELWARE

4.74 42.7 12 3.56 1.37 1.52 4.14 4.09

NIGERIA ENAMELWARE

0.63 45.7 12 3.81 0.35 0.39 4.67 4.12

NIGERIA ENAMELWARE

1.04 41.8 12 3.48 0.53 0.59 4.46 4.08

NIGERIA ENAMELWARE

0.74 6.04 0 0.02 0.02 7.06 5.35

NIGERIA ENAMELWARE

0.61 0.93 0 0.05 0.09 6.88 5.53

NIGERIA ENAMELWARE

0.77 1.86 0.03 62 0.19 0.26 6.52 5.75

NIGERIA ENAMELWARE

1.02 0.73 0 0.08 0.27 6.24 5.13

NIGERIA ENAMELWARE

0.88 0.41 0 0.00 0.01 6.11 5.16

BETA GLASS COMPANY

0.09 2.95 0 0.16 0.18 7.06 6.17

BETA GLASS COMPANY

0.09 2.77 0 0.18 0.19 7.01 6.14

BETA GLASS COMPANY

0.09 2.39 0 0.13 0.14 7.06 6.08

BETA GLASS COMPANY

0.09 1.91 0 0.09 0.10 7.06 5.94

154

BETA GLASS COMPANY

0.09 0.84 0 0.06 0.07 6.90 5.58

BETA GLASS COMPANY

0.09 14.83 0 0.14 0.15 7.23 6.07

BETA GLASS COMPANY

0.09 2.74 0 0.04 0.04 7.26 5.34

BETA GLASS COMPANY

0.09 10.3 0 0.06 0.07 7.16 5.91

BETA GLASS COMPANY

0.09 3.02 0 0.09 0.10 6.90 5.80

FLOUR MILL 0.45 9.67 2 4.84 0.32 0.35 7.90 7.23

FLOUR MILL 0.42 2.23 0.5 4.46 0.09 0.10 7.79 #VALUE!

FLOUR MILL 0.30 4.08 1 4.08 0.20 0.22 7.70 6.80

FLOUR MILL 0.25 4.81 0.9 5.34 0.22 0.23 7.65 6.87

FLOUR MILL 0.37 4.01 0.85 4.72 0.05 0.05 7.53 6.67

FLOUR MILL 0.36 1.26 0.7 1.8 0.10 0.11 7.41 6.16

FLOUR MILL 0.48 1.88 0.7 2.69 0.20 0.25 7.22 6.14

FLOUR MILL 0.47 0.35 0.4 0.88 0.21 0.26 7.11 5.41

FLOUR MILL 0.25 2.82 0.75 3.76 0.02 0.02 6.96 6.19

FLOUR MILL 2.50 2.76 0 0.98 -0.66 5.73 5.61

FLOUR MILL 4.13 1.59 0 0.68 -0.22 5.66 5.37

FLOUR MILL 3.98 0.39 0 0.17 -0.06 5.63 4.76

FLOUR MILL 3.15 0.7 0 0.22 -0.10 5.64 5.02

FLOUR MILL 0.03 0.37 0.25 1.48 0.23 0.23 5.57 4.74

FLOUR MILL 0.03 0.99 0.5 1.98 0.76 0.78 5.45 5.17

FLOUR MILL 0.03 0.93 0.22 4.23 1.16 1.19 5.24 5.14

FLOUR MILL 0.03 1 0.25 4 1.67 1.71 5.12 5.17

NATIONAL SALT COMPANY

0.88 0.62 0 0.62 5.25 6.52 6.22

NATIONAL SALT COMPANY

0.65 0.79 0.7 1.13 0.07 0.20 6.58 6.27

NATIONAL SALT COMPANY

0.96 0.49 0.49 1 0.75 21.03 6.40 6.11

NATIONAL SALT COMPANY

1.29 0.57 0.48 1.19 0.95 -3.32 6.27 6.10

NATIONAL SALT COMPANY

2.77 0.19 0.01 19 0.19 -0.11 4.89 4.17

NATIONAL SALT COMPANY

0.61 0 0 0.14 0.34 4.93 4.43

NATIONAL SALT COMPANY

0.50 0 0 0.16 0.31 4.98 4.06

NATIONAL SALT COMPANY

0.37 0 0 0.11 0.18 5.01 4.17

NATIONAL SALT COMPANY

0.32 0 0 0.16 0.23 5.07 4.06

P.S. MANDRIDES PLC 0.03 0 0 0.24 0.25 5.11 4.46

P.S. MANDRIDES PLC 0.42 5.34 0 0.10 0.17 5.53 5.51

P.S. MANDRIDES PLC 0.40 7.69 0 0.14 0.23 5.53 4.54

P.S. MANDRIDES PLC 0.40 1.55 0 0.04 0.06 5.46 3.79

155

P.S. MANDRIDES PLC 0.38 2.09 0 0.03 0.05 5.44 3.92

P.S. MANDRIDES PLC 0.41 4.3 0 0.10 0.17 5.45 4.24

GUINNESS 0.08 19.31 7.5 2.57 0.40 0.44 7.70 7.12

GUINNESS 0.08 19.18 12.8 1.5 0.41 0.44 7.67 7.13

GUINNESS 0.08 8.04 4.5 1.79 0.36 0.39 7.68 7.07

GUINNESS 0.08 7.84 3.4 2.31 0.38 0.41 7.59 7.03

GUINNESS 0.08 6.31 4 1.58 0.30 0.32 7.58 6.87

GUINNESS 0.08 4.12 3 1.37 0.17 0.18 7.58 6.69

GUINNESS 0.08 6.71 5.25 1.28 0.36 0.39 7.51 6.90

GUINNESS 0.08 5.62 7.92 0.71 0.48 0.52 7.32 6.82

GUINNESS 0.08 3.52 3.05 1.15 0.35 0.38 7.22 6.62

INTERNATIONAL BREWERIES

0.08 0.09 0 0.02 0.02 6.94 5.30

INTERNATIONAL BREWERIES

0.08 0.14 0 0.07 0.08 6.60 5.46

INTERNATIONAL BREWERIES

0.08 0.03 0 0.05 0.06 6.09 4.80

INTERNATIONAL BREWERIES

0.08 0.23 0 0.45 0.49 5.42 5.07

INTERNATIONAL BREWERIES

0.08 0.43 0 1.14 1.24 5.50 5.56

INTERNATIONAL BREWERIES

0.08 1.02 0 1.57 1.70 5.52 5.72

INTERNATIONAL BREWERIES

0.08 0.47 0 0.62 0.68 5.59 5.38

INTERNATIONAL BREWERIES

0.08 0.28 0 0.38 0.42 5.57 5.15

INTERNATIONAL BREWERIES

0.08 0.19 0 4.63 5.03 5.33 6.00

NIGERIA BREWERIES 0.05 4.01 3.54 1.13 0.47 0.49 7.98 #VALUE!

NIGERIA BREWERIES 0.05 3.69 1.8 2.05 0.46 0.49 7.95 7.45

NIGERIA BREWERIES 0.17 3.4 2.85 1.19 0.45 0.55 7.92 7.41

NIGERIA BREWERIES 0.08 2.5 1.59 1.57 0.43 0.46 7.81 7.28

NIGERIA BREWERIES 0.08 1.44 1.2 1.2 0.25 0.28 7.81 7.04

NIGERIA BREWERIES 0.11 1.09 0.65 1.68 0.19 0.21 7.83 6.92

NIGERIA BREWERIES 0.21 0.67 0.56 1.2 0.13 0.16 7.85 6.71

NIGERIA BREWERIES 0.21 1.94 1.3 1.49 0.17 0.21 7.81 6.87

NIGERIA BREWERIES 0.09 1.93 1.12 1.72 0.22 0.24 7.68 6.86

7UP 0.41 3.43 1.5 2.29 0.10 0.11 7.43 6.36

7UP 0.39 2.98 1.5 1.99 0.09 0.10 7.38 6.18

7UP 0.39 3.14 1.3 2.42 0.13 0.14 7.27 6.21

7UP 0.38 2.38 1.25 1.9 0.13 0.14 7.16 6.09

7UP 0.07 0 0 0.00 0.00 7.22 6.25

7UP 0.84 2.33 1.25 1.86 0.16 0.17 6.98 5.98

7UP 0.84 2.79 1 2.79 0.26 0.28 6.82 6.06

156

7UP 0.84 3.37 0.75 4.49 0.38 0.41 6.72 6.14

7UP 0.84 2.81 0.6 4.68 0.60 0.65 6.45 6.06

7UP 0.84 0.87 0.4 2.18 0.33 0.36 6.25 5.60

DANGOTE FLOUR MILL

0.07 0.54 0 0.18 0.20 7.73 #VALUE!

DANGOTE FLOUR MILL

0.07 1.11 0 0.14 0.16 7.66 6.73

DANGOTE FLOUR MILL

0.07 0.69 0 0.19 0.20 7.63 6.50

DANGOTE FLOUR MILL

0.08 0.11 0 0.13 0.14 7.55 5.83

DANGOTE SUGAR 0.07 0.94 0 0.79 0.85 7.31 7.05

DANGOTE SUGAR 0.07 1 0 0.89 0.96 7.34 7.12

DANGOTE SUGAR 0.07 1.82 0 2.15 2.32 7.26 7.34

DANGOTE SUGAR 0.07 2.15 0 1.71 1.85 7.25 7.33

DANGOTE SUGAR 0.07 1.67 0 0.90 0.97 7.27 7.22

Source: Researcher’s Excel Summary of Ratio Data of Model Proxies

157

Appendix Two: Quantum Values of Model Proxies

COMPANY NAME FA CA TA EBIPTA PAT CL TD SHF

UAC PLC(H’M) 51,572,000.00 15,471,600.00 67,043,600.00 7,038,000.00 3,191,000.00 10,520,688.00 10520688 56,522,912.00

UAC PLC(H’M) 54,472,000.00 16,341,600.00 70,813,600.00 7,568,000.00 4,019,000.00 11,112,288.00 11112288 59,701,312.00

UAC PLC(H’M) 70,575,000.00 21,172,500.00 91,747,500.00 8,527,000.00 4,241,000.00 14,397,300.00 14397300 77,350,200.00

UAC PLC(H’M) 54,715,000.00 16,414,500.00 71,129,500.00 4,403,000.00 3,058,000.00 11,161,860.00 11161860 59,967,640.00

UAC PLC(H’M) 12,668,000.00 3,800,400.00 16,468,400.00 4,589,000.00 3,204,000.00 2,584,272.00 2584272 13,884,128.00

UAC PLC(H’M) 11,232,000.00 3,369,600.00 14,601,600.00 4,528,900.00 1,629,900.00 2,291,328.00 2291328 12,310,272.00

UAC PLC(H’M) 9,824,000.00 2,947,200.00 12,771,200.00 5,628,000.00 1,570,100.00 2,004,096.00 2004096 10,767,104.00

UAC PLC(H’M) 9,587,600.00 2,876,280.00 12,463,880.00 6,729,000.00 2,184,600.00 1,955,870.40 1955870.4 10,508,009.60

UAC PLC(H’M) 9,101,800.00 2,730,540.00 11,832,340.00 5,587,290.00 1,166,200.00 1,856,767.20 1856767.2 9,975,572.80

UAC PLC(H’M) 6,750,400.00 2,025,120.00 8,775,520.00 6,529,800.00 1,006,200.00 1,377,081.60 1377081.6 7,398,438.40

UAC PLC(H’M) 4,347,700.00 1,304,310.00 5,652,010.00 7,832,000.00 4,105,800.00 886,930.80 886930.8 4,765,079.20

UAC PLC(H’M) 3,685,900.00 1,105,770.00 4,791,670.00 6,834,560.00 3,114,300.00 751,923.60 751923.6 4,039,746.40

UAC PLC(H’M) 3,881,600.00 1,164,480.00 5,046,080.00 6,321,000.00 4,800,700.00 791,846.40 791846.4 4,254,233.60

UAC PLC(H’M) 8,319,800.00 2,495,940.00 10,815,740.00 6,456,800.00 4,399,400.00 1,697,239.20 1697239.2 9,118,500.80

ARBICO 935,551.00 280,665.30 1,216,216.30 107,990.00 50,990.00 190,852.40 190852.4 1,025,363.90

ARBICO 891,433.00 267,429.90 1,158,862.90 26,635.00 12,530.00 181,852.33 181852.33 977,010.57

ARBICO 235,491.00 70,647.30 306,138.30 60,670.00 32,700.00 48,040.16 48040.164 258,098.14

ARBICO 247,903.00 74,370.90 322,273.90 10,329.00 8,213.00 50,572.21 50572.212 271,701.69

ARBICO 83,627.00 25,088.10 108,715.10 11,577.00 8,498.00 17,059.91 17059.908 91,655.19

ARBICO 85,653.00 25,695.90 111,348.90 11,370.00 7,780.00 17,473.21 17473.212 93,875.69

ARBICO 93,784.00 28,135.20 121,919.20 12,567.00 9,850.00 19,131.94 19131.936 102,787.26

ARBICO 102,478.00 30,743.40 133,221.40 13,894.00 10,893.00 20,905.51 20905.512 112,315.89

ARBICO 89,453.00 26,835.90 116,288.90 14,563.00 9,345.00 18,248.41 18248.412 98,040.49

ARBICO 27,392.00 8,217.60 35,609.60 15,670.00 11,250.00 5,587.97 5845.968 30,021.63

158

ARBICO 25,163.00 7,548.90 32,711.90 16,350.00 13,678.00 5,133.25 26492.252 27,578.65

ARBICO 25,571.00 7,671.30 33,242.30 16,700.00 14,530.00 5,216.48 15787.484 28,025.82

ARBICO 28,033.00 8,409.90 36,442.90 18,300.00 16,830.00 5,718.73 33751.732 30,724.17

ARBICO 29,436.00 8,830.80 38,266.80 17,345.00 12,673.00 6,004.94 35440.944 32,261.86

ARBICO 30,125.00 9,037.50 39,162.50 18,900.00 17,890.00 6,145.50 36270.5 33,017.00

CAPPA& D’ALBERTO PLC (N 000)

885,326.00 265,597.80 1,150,923.80 740,828.00 506,218.00 180,606.50 180606.5 970,317.30

CAPPA& D’ALBERTO PLC (N 000)

721,097.00 216,329.10 937,426.10 121880 836,150.00 147,103.79 147103.79 790,322.31

CAPPA& D’ALBERTO PLC (N 000)

491,557.00 147,467.10 639,024.10 501,142.00 127,946.00 100,277.63 100277.63 538,746.47

CAPPA& D’ALBERTO PLC (N 000)

485,188.00 145,556.40 630,744.40 263,021.00 197,373.00 98,978.35 98978.352 531,766.05

CAPPA& D’ALBERTO PLC (N 000)

472,084.00 141,625.20 613,709.20 146,449.00 126,144.00 96,305.14 96305.136 517,404.06

CAPPA& D’ALBERTO PLC (N 000)

485,188.00 145,556.40 630,744.40 263,021.00 197,373.00 98,978.35 98978.352 531,766.05

CAPPA& D’ALBERTO PLC (N 000)

472,084.00 141,625.20 613,709.20 146,449.00 126,114.00 96,305.14 96305.136 517,404.06

CAPPA& D’ALBERTO PLC (N 000)

509,583.00 152,874.90 662,457.90 156,367.00 111,389.00 103,954.93 103954.93 558,502.97

CAPPA& D’ALBERTO PLC (N 000)

579,563.00 173,868.90 753,431.90 176,500.00 156,900.00 118,230.85 118230.85 635,201.05

CAPPA& D’ALBERTO PLC (N 000)

639,021.00 191,706.30 830,727.30 187,900.00 163,000.00 130,360.28 130360.28 700,367.02

CAPPA& D’ALBERTO PLC (N 000)

270,342.00 81,102.60 351,444.60 150,792.00 118,091.00 55,149.77 55149.768 296,294.83

CAPPA& D’ALBERTO PLC (N 000)

272,861.00 81,858.30 354,719.30 107,031.00 68,542.00 55,663.64 55663.644 299,055.66

CAPPA& D’ALBERTO PLC (N 000)

261,845.00 78,553.50 340,398.50 126,213.00 92,136.00 53,416.38 53416.38 286,982.12

CAPPA& D’ALBERTO PLC (N 000)

254,667.00 76,400.10 331,067.10 113,767.00 90,899.00 51,952.07 51952.068 279,115.03

CAPPA& D’ALBERTO PLC (N 000)

86,187.00 25,856.10 112,043.10 142,335.00 109.33 17,582.15 17582.148 94,460.95

COSTAIN 5,44,135 9803547 133499 53,273.00 33,402.00 6730000 670013 342880

COSTAIN 3,531,285.00 1,059,385.50 4,590,670.50 574,787.00 615,124.00 720,382.14 720382.14 3,870,288.36

COSTAIN 1,746,542.00 523,962.60 2,270,504.60 380,516.00 353,107.00 356,294.57 356294.57 1,914,210.03

159

COSTAIN 1,108,782.00 332,634.60 1,441,416.60 114,263.00 107,963.00 226,191.53 226191.53 1,215,225.07

COSTAIN 1,060,933.00 318,279.90 1,379,212.90 1,488,639.00 1,488,639.00 216,430.33 216430.33 1,162,782.57

COSTAIN 1,169,336.00 350,800.80 1,520,136.80 1,488,639.00 2,81,347 238,544.54 238544.54 1,281,592.26

COSTAIN 875,699.00 262,709.70 1,138,408.70 80,753.00 469,010.00 178,642.60 178642.6 959,766.10

COSTAIN 516,522.00 154,956.60 671,478.60 42,306.00 42,605.00 105,370.49 105370.49 566,108.11

COSTAIN 385,560.00 115,668.00 501,228.00 22,655.00 20,048.00 78,654.24 78654.24 422,573.76

COSTAIN 453,894.00 136,168.20 590,062.20 34,590.00 25,780.00 92,594.38 92594.376 497,467.82

COSTAIN 453,010.00 135,903.00 588,913.00 229,589.00 231,591.00 92,414.04 92414.04 496,498.96

COSTAIN 624,419.00 187,325.70 811,744.70 291,042.00 291,213.00 127,381.48 127381.48 684,363.22

COSTAIN 348,723.00 104,616.90 453,339.90 10,869.00 3,225.00 71,139.49 71139.492 382,200.41

COSTAIN 261,837.00 78,551.10 340,388.10 54,526.00 45,200.00 53,414.75 53414.748 286,973.35

COSTAIN 247,523.00 74,256.90 321,779.90 42,307.00 35,164.00 50,494.69 50494.692 271,285.21

G. CAPPA 6,653,943.00 1,996,182.90 8,650,125.90 50,160.00 50,725.00 1,357,404.37 1357404.4 7,292,721.53

G. CAPPA 1,532,086.00 459,625.80 1,991,711.80 142,102.00 142,356.00 312,545.54 312545.54 1,679,166.26

G. CAPPA 1,645,597.00 493,679.10 2,139,276.10 14,404.00 17,924.00 335,701.79 335701.79 1,803,574.31

G. CAPPA 1,680,025.00 504,007.50 2,184,032.50 246,593.00 246,917.00 342,725.10 342725.1 1,841,307.40

G. CAPPA 1,724,726.00 517,417.80 2,242,143.80 327,534.00 327,655.00 351,844.10 351844.1 1,890,299.70

G. CAPPA 1,768,490.00 530,547.00 2,299,037.00 453,902.00 259,000.00 360,771.96 360771.96 1,938,265.04

G. CAPPA 1,893,562.00 568,068.60 2,461,630.60 453,700.00 267,300.00 386,286.65 386286.65 2,075,343.95

G. CAPPA 1,894,523.00 568,356.90 2,462,879.90 562,000.00 345,700.00 386,482.69 386482.69 2,076,397.21

G. CAPPA 1,904,567.00 571,370.10 2,475,937.10 769,000.00 569,600.00 388,531.67 388531.67 2,087,405.43

G. CAPPA 1,679,300.00 503,790.00 2,183,090.00 233,456.00 123,450.00 342,577.20 342577.2 1,840,512.80

G. CAPPA 447,622.00 134,286.60 581,908.60 139,383.00 139,383.00 91,314.89 91314.888 490,593.71

G. CAPPA 406,290.00 121,887.00 528,177.00 50,256.00 50,256.00 82,883.16 82883.16 445,293.84

G. CAPPA 230,551.00 69,165.30 299,716.30 180,856.00 180,656.00 47,032.40 47032.404 252,683.90

G. CAPPA 182,650.00 54,795.00 237,445.00 153,758.00 143,758.00 37,260.60 37260.6 200,184.40

160

G. CAPPA 144,016.00 43,204.80 187,220.80 105,347.00 85,347.00 29,379.26 29379.264 157,841.54

ROADS NIGERIA 909,067.00 272,720.10 1,181,787.10 121,972.00 73,197.00 185,449.67 185449.67 996,337.43

ROADS NIGERIA 917,786.00 275,335.80 1,193,121.80 12,552.00 8,208.00 187,228.34 187228.34 1,005,893.46

ROADS NIGERIA 1,020,788.00 306,236.40 1,327,024.40 86,339.00 59,797.00 208,240.75 208240.75 1,118,783.65

ROADS NIGERIA 332,992.00 99,897.60 432,889.60 74,208.00 41,341.00 67,930.37 67930.368 364,959.23

ROADS NIGERIA 139,976.00 41,992.80 181,968.80 33,442.00 25,122.00 28,555.10 28555.104 153,413.70

ROADS NIGERIA 114,784.00 34,435.20 149,219.20 11,559.00 9,676.00 23,415.94 23415.936 125,803.26

ROADS NIGERIA 108,085.00 32,425.50 140,510.50 1,421.00 4,783.00 22,049.34 22049.34 118,461.16

ROADS NIGERIA 159,567.00 47,870.10 207,437.10 8,578.00 5,919.00 32,551.67 32551.668 174,885.43

ROADS NIGERIA 210,770.00 63,231.00 274,001.00 12,805.00 9,780.00 42,997.08 42997.08 231,003.92

ROADS NIGERIA 342,890.00 102,867.00 445,757.00 12,006.00 8,930.00 69,949.56 69949.56 375,807.44

ROADS NIGERIA 342,275.00 102,682.50 444,957.50 11,768.00 7,680.00 69,824.10 69824.1 375,133.40

ROADS NIGERIA 382,365.00 114,709.50 497,074.50 10,262.00 3,975.00 78,002.46 78002.46 419,072.04

ROADS NIGERIA 490,229.00 147,068.70 637,297.70 14,114.00 7,530.00 100,006.72 100006.72 537,290.98

ROADS NIGERIA 391,676.00 117,502.80 509,178.80 9,674.00 8,165.00 79,901.90 79901.904 429,276.90

ROADS NIGERIA 17,532.00 5,259.60 22,791.60 1,118.00 781 3,576.53 3576.528 19,215.07

ROADS NIGERIA 11,510.00 3,453.00 14,963.00 4,655.00 3,777.00 2,348.04 2348.04 12,614.96

UACN PROPERTY 37,968,735.00 11,390,620.50 49,359,355.50 2,538,771.00 2,278,026.00 7,745,621.94 7745621.9 41,613,733.56

UACN PROPERTY 40,468,617.00 12,140,585.10 52,609,202.10 2,828,321.00 2,386,339.00 8,255,597.87 8255597.9 44,353,604.23

UACN PROPERTY 41,680,867.00 12,504,260.10 54,185,127.10 3,716,592.00 3,682,867.00 8,502,896.87 8502896.9 45,682,230.23

UACN PROPERTY 43,036,643.00 12,910,992.90 55,947,635.90 773,616.00 425,284.00 8,779,475.17 8779475.2 47,168,160.73

UACN PROPERTY 28,099,025.00 8,429,707.50 36,528,732.50 1,368,898.00 962,395.00 5,732,201.10 5732201.1 30,796,531.40

UACN PROPERTY 21,117,433.00 6,335,229.90 27,452,662.90 1,003,069.00 834,259.00 4,307,956.33 4307956.3 23,144,706.57

UACN PROPERTY 18,993,980.00 5,698,194.00 24,692,174.00 665,255.00 458,082.00 3,874,771.92 3874771.9 20,817,402.08

UACN PROPERTY 15,361,233.00 4,608,369.90 19,969,602.90 1,063,654.00 918,150.00 3,133,691.53 3133691.5 16,835,911.37

UACN PROPERTY 14,295,466.00 4,288,639.80 18,584,105.80 846,973.00 740,274.00 2,916,275.06 2916275.1 15,667,830.74

161

UACN PROPERTY 12,784,567.00 3,835,370.10 16,619,937.10 785,346.00 673,000.00 2,608,051.67 2608051.7 14,011,885.43

UACN PROPERTY 6,830,366.00 2,049,109.80 8,879,475.80 582,980.00 488,488.00 1,393,394.66 1393394.7 7,486,081.14

UACN PROPERTY 6,961,006.00 2,088,301.80 9,049,307.80 198,381.00 154,692.00 1,420,045.22 1420045.2 7,629,262.58

UACN PROPERTY 5,454,283.00 1,636,284.90 7,090,567.90 171,068.00 131,943.00 1,112,673.73 1112673.7 5,977,894.17

DN TYRE & RUBBER PLC

4,766,082.00 1,429,824.60 6,195,906.60 943,025.00 388,127.00 972,280.73 972280.73 5,223,625.87

DN TYRE & RUBBER PLC

4,987,650.00 1,496,295.00 6,483,945.00 4,820,485.00 11,143,551.00 1,017,480.60 1017480.6 5,466,464.40

DN TYRE & RUBBER PLC

15,743,307.00 4,722,992.10 20,466,299.10 2,088,126.00 2,093,004.00 3,211,634.63 3211634.6 17,254,664.47

DN TYRE & RUBBER PLC

15,272,163.00 4,581,648.90 19,853,811.90 653,472.00 646,744.00 3,115,521.25 3115521.3 16,738,290.65

DN TYRE & RUBBER PLC

13,000,436.00 3,900,130.80 16,900,566.80 205,445.00 246,456.00 2,652,088.94 2652088.9 14,248,477.86

CHAMPIONS BREWERIES

2,076,182.00 622,854.60 2,699,036.60 858,166.00 1,237,196.00 423,541.13 423541.13 2,275,495.47

CHAMPIONS BREWERIES

2,497,944.00 749,383.20 3,247,327.20 1,015,094.00 915,788.00 509,580.58 509580.58 2,737,746.62

CHAMPIONS BREWERIES

2,915,744.00 874,723.20 3,790,467.20 857,027.00 651,027.00 594,811.78 594811.78 3,195,655.42

CHAMPIONS BREWERIES

3,369,754.00 1,010,926.20 4,380,680.20 330,737.00 230,737.00 687,429.82 687429.82 3,693,250.38

CHAMPIONS BREWERIES

2,986,206.00 895,861.80 3,882,067.80 422,544.00 322,544.00 609,186.02 609186.02 3,272,881.78

CHAMPIONS BREWERIES

2,320,191.00 696,057.30 3,016,248.30 96,967.00 76,967.00 473,318.96 473318.96 2,542,929.34

CHAMPIONS BREWERIES

685,556.00 205,666.80 891,222.80 166,807.00 142,594.00 139,853.42 139853.42 751,369.38

CHAMPIONS BREWERIES

590,533.00 177,159.90 767,692.90 104,739.00 94,739.00 120,468.73 120468.73 647,224.17

CHAMPIONS BREWERIES

419,031.00 125,709.30 544,740.30 64,123.00 54,123.00 85,482.32 85482.324 459,257.98

CHAMPIONS BREWERIES

389,075.00 116,722.50 505,797.50 125,641.00 106,087.00 79,371.30 79371.3 426,426.20

GOLDEN BREWERIES 2,254,597.00 676,379.10 2,930,976.10 569,671.00 460,570.00 459,937.79 459937.79 2,471,038.31

GOLDEN BREWERIES 2,357,584.00 707,275.20 3,064,859.20 387,603.00 288,971.00 480,947.14 480947.14 2,583,912.06

GOLDEN BREWERIES 1,041,912.00 312,573.60 1,354,485.60 11,907.00 7,650.00 212,550.05 212550.05 1,141,935.55

GOLDEN BREWERIES 1,311,406.00 393,421.80 1,704,827.80 110,837.00 96,158.00 267,526.82 267526.82 1,437,300.98

162

GOLDEN BREWERIES 1,301,621.00 390,486.30 1,692,107.30 188,160.00 162,650.00 265,530.68 265530.68 1,426,576.62

GOLDEN BREWERIES 1,453,670.00 436,101.00 1,889,771.00 189,340.00 155,900.00 296,548.68 296548.68 1,593,222.32

GOLDEN BREWERIES 2,254,597.00 676,379.10 2,930,976.10 569,671.00 570,004.00 459,937.79 459937.79 2,471,038.31

GOLDEN BREWERIES 2,357,584.00 707,275.20 3,064,859.20 387,603.00 388,971.00 480,947.14 480947.14 2,583,912.06

GOLDEN BREWERIES 1,041,912.00 312,573.60 1,354,485.60 11,907.00 7,650.00 212,550.05 212550.05 1,141,935.55

GOLDEN BREWERIES 1,311,406.00 393,421.80 1,704,827.80 110,837.00 116,158.00 267,526.82 267526.82 1,437,300.98

GOLDEN BREWERIES 301,621.00 90,486.30 392,107.30 4,490.00 1,920.65 61,530.68 61530.684 330,576.62

GOLDEN BREWERIES 301,621.00 90,486.30 392,107.30 188,160.00 162,158.00 61,530.68 61530.684 330,576.62

GOLDEN BREWERIES 265,192.00 79,557.60 344,749.60 8,844.00 10,367.00 54,099.17 54099.168 290,650.43

GOLDEN BREWERIES 197,527.00 59,258.10 256,785.10 9,729.00 6,921.00 40,295.51 40295.508 216,489.59

GOLDEN BREWERIES 131,793.00 39,537.90 171,330.90 57,945.00 45,966.00 26,885.77 26885.772 144,445.13

A.G LEVENTIS 9,717,546.00 2,915,263.80 12,632,809.80 787,562.00 648,243.00 1,982,379.38 1982379.4 10,650,430.42

A.G LEVENTIS 10,001,620.00 3,000,486.00 13,002,106.00 1,763,235.00 1,234,998.00 2,040,330.48 2040330.5 10,961,775.52

A.G LEVENTIS 8,436,297.00 2,530,889.10 10,967,186.10 1,743,537.00 1,218,171.00 1,721,004.59 1721004.6 9,246,181.51

A.G LEVENTIS 5,368,815.00 1,610,644.50 6,979,459.50 1,039,628.00 752,874.00 1,095,238.26 1095238.3 5,884,221.24

A.G LEVENTIS 2,890,954.00 867,286.20 3,758,240.20 706,103.00 468,000.00 589,754.62 589754.62 3,168,485.58

A.G LEVENTIS 2,891,964.00 867,589.20 3,759,553.20 593,301.00 382,270.00 589,960.66 589960.66 3,169,592.54

A.G LEVENTIS 27,809,292.00 8,342,787.60 36,152,079.60 325,825.00 240,992.00 5,673,095.57 5673095.6 30,478,984.03

A.G LEVENTIS 8,837,932.00 2,651,379.60 11,489,311.60 305,663.00 186,180.00 1,802,938.13 1802938.1 9,686,373.47

A.G LEVENTIS 876,986.00 263,095.80 1,140,081.80 132,714.00 59,565.00 178,905.14 178905.14 961,176.66

A.G LEVENTIS 1,856,170.00 556,851.00 2,413,021.00 83,090.00 36,310.00 378,658.68 378658.68 2,034,362.32

A.G LEVENTIS 1,589,669.00 476,900.70 2,066,569.70 41,088.00 10,779.00 324,292.48 336765.48 1,742,277.22

A.G LEVENTIS 1,500,020.00 450,006.00 1,950,026.00 102,899.00 70,557.00 306,004.08 318477.08 1,644,021.92

A.G LEVENTIS 1,231,902.00 369,570.60 1,601,472.60 1,196.00 10,209.00 251,308.01 278059.01 1,350,164.59

A.G LEVENTIS 1,056,361.00 316,908.30 1,373,269.30 102,952.00 89,573.00 215,497.64 259724.64 1,157,771.66

CHELLARAMS PLC 3,306,178.00 991,853.40 4,298,031.40 333,821.00 220,618.00 674,460.31 674460.31 3,623,571.09

163

CHELLARAMS PLC 3,081,192.00 924,357.60 4,005,549.60 415,633.00 446,125.00 628,563.17 628563.17 3,376,986.43

CHELLARAMS PLC 2,814,080.00 844,224.00 3,658,304.00 100,955.00 376,896.00 574,072.32 574072.32 3,084,231.68

CHELLARAMS PLC 2,786,071.00 835,821.30 3,621,892.30 311,323.00 256,405.00 568,358.48 568358.48 3,053,533.82

CHELLARAMS PLC 2,148,860.00 644,658.00 2,793,518.00 215,587.00 277,593.00 438,367.44 438367.44 2,355,150.56

CHELLARAMS PLC 1,931,010.00 579,303.00 2,510,313.00 105,591.00 32,143.00 393,926.04 393926.04 2,116,386.96

CHELLARAMS PLC 1,326,728.00 398,018.40 1,724,746.40 91,553.00 56,127.00 270,652.51 270652.51 1,454,093.89

CHELLARAMS PLC 936,117.00 280,835.10 1,216,952.10 67,640.00 42,466.00 190,967.87 190967.87 1,025,984.23

CHELLARAMS PLC 497,387.00 149,216.10 646,603.10 46,916.00 31,305.00 101,466.95 101466.95 545,136.15

CHELLARAMS PLC 497,387.00 149,216.10 646,603.10 387,018.00 23,845.00 101,466.95 101466.95 545,136.15

CHELLARAMS PLC 452,782.00 135,834.60 588,616.60 31,810.00 25,735.00 92,367.53 92367.528 496,249.07

CHELLARAMS PLC 388,395.00 116,518.50 504,913.50 36,097.00 29,297.00 79,232.58 79232.58 425,680.92

CHELLARAMS PLC 327,052.00 98,115.60 425,167.60 23,335.00 18,481.00 66,718.61 66718.608 358,448.99

CHELLARAMS PLC 198,584.00 59,575.20 258,159.20 19,783.00 13,943.00 40,511.14 40511.136 217,648.06

JOHN HOLTS 7,929,000.00 2,378,700.00 10,307,700.00 5,000.00 10,000.00 1,617,516.00 1617516 8,690,184.00

JOHN HOLTS 7,093,000.00 2,127,900.00 9,220,900.00 25,000.00 2,144,000.00 1,446,972.00 1446972 7,773,928.00

JOHN HOLTS 5,240,000.00 1,572,000.00 6,812,000.00 62,000.00 390,000.00 1,068,960.00 1068960 5,743,040.00

JOHN HOLTS 4,097,000.00 1,229,100.00 5,326,100.00 95,000.00 38,000.00 835,788.00 835788 4,490,312.00

JOHN HOLTS 3,515,000.00 1,054,500.00 4,569,500.00 376,000.00 476,000.00 717,060.00 717060 3,852,440.00

JOHN HOLTS 3,536,000.00 1,060,800.00 4,596,800.00 15,000.00 25,000.00 721,344.00 721344 3,875,456.00

JOHN HOLTS 2,722,000.00 816,600.00 3,538,600.00 245,000.00 70,000.00 555,288.00 555288 2,983,312.00

JOHN HOLTS 3,478,000.00 1,043,400.00 4,521,400.00 133,000.00 218,000.00 709,512.00 709512 3,811,888.00

JOHN HOLTS 2,632,000.00 789,600.00 3,421,600.00 276,000.00 179,000.00 536,928.00 536928 2,884,672.00

JOHN HOLTS 2,048,000.00 614,400.00 2,662,400.00 258,000.00 135,000.00 417,792.00 417792 2,244,608.00

JOHN HOLTS 1,957,000.00 587,100.00 2,544,100.00 57,000.00 75,000.00 399,228.00 399228 2,144,872.00

JOHN HOLTS 2,100,000.00 630,000.00 2,730,000.00 1,628,000.00 1,772,000.00 428,400.00 428400 2,301,600.00

JOHN HOLTS 2,208,000.00 662,400.00 2,870,400.00 155,000.00 70,000.00 450,432.00 450432 2,419,968.00

164

JOHN HOLTS 1,621,000.00 486,300.00 2,107,300.00 347,000.00 263,000.00 330,684.00 330684 1,776,616.00

SCOA NIGERIA PLC 769,000.00 230,700.00 999,700.00 221,000.00 213,000.00 156,876.00 156876 842,824.00

SCOA NIGERIA PLC 804,000.00 241,200.00 1,045,200.00 811,000.00 714,000.00 164,016.00 164016 881,184.00

SCOA NIGERIA PLC 992,000.00 297,600.00 1,289,600.00 284,000.00 232,000.00 202,368.00 202368 1,087,232.00

SCOA NIGERIA PLC 856,000.00 256,800.00 1,112,800.00 1,002,000.00 822,000.00 174,624.00 174624 938,176.00

SCOA NIGERIA PLC 451,000.00 135,300.00 586,300.00 821,000.00 705,000.00 92,004.00 92004 494,296.00

SCOA NIGERIA PLC 221,000.00 66,300.00 287,300.00 860,000.00 867,000.00 45,084.00 45084 242,216.00

SCOA NIGERIA PLC 261,000.00 78,300.00 339,300.00 348,000.00 327,000.00 53,244.00 53244 286,056.00

SCOA NIGERIA PLC 231,000.00 69,300.00 300,300.00 65,000.00 42,000.00 47,124.00 47124 253,176.00

SCOA NIGERIA PLC 283,000.00 84,900.00 367,900.00 113,000.00 104,000.00 57,732.00 57732 310,168.00

SCOA NIGERIA PLC 258,000.00 77,400.00 335,400.00 260,000.00 177,000.00 52,632.00 159632 282,768.00

SCOA NIGERIA PLC 209,000.00 62,700.00 271,700.00 214,000.00 166,000.00 42,636.00 137636 229,064.00

SCOA NIGERIA PLC 284,000.00 85,200.00 369,200.00 115,000.00 88,000.00 57,936.00 129936 311,264.00

SCOA NIGERIA PLC 325,000.00 97,500.00 422,500.00 47,000.00 34,000.00 66,300.00 119300 356,200.00

SCOA NIGERIA PLC 466,000.00 139,800.00 605,800.00 78,000.00 58,000.00 95,064.00 182064 510,736.00

TRANSCORP 30,329,672.00 9,098,901.60 39,428,573.60 6,908,216.00 5,389,786.00 12,604,157 12604157 26,824,416.60

TRANSCORP 25,347,658.00 7,604,297.40 32,951,955.40 3,233,160.00 1,226,577.00 9,855,460 9855460 23,096,495.40

TRANSCORP 88,776,440.00 26,632,932.00 115,409,372.00 3,760,254.00 5,127,825.00 74,179,033 74179033 41,230,339.00

TRANSCORP 89,432,098.00 26,829,629.40 116,261,727.40 6,861,825.00 7,870,788.00 75,195,638 75195638 41,066,089.40

TRANSCORP 89,970,345.00 26,991,103.50 116,961,448.50 8,080,754.00 9,364,799.00 77,886,234 77886234 39,075,214.50

DANGOTE CEMENT 255,442,982.00 76,632,894.60 332,075,876.60 101,133,468.00 106,605,409.00 52,110,368.33 150361781 279,965,508.27

DANGOTE CEMENT 186,393,346.00 55,918,003.80 242,311,349.80 63,775,871.00 61,392,230.00 38,024,242.58 87644040 204,287,107.22

DANGOTE CEMENT 142,388,500.00 42,716,550.00 185,105,050.00 49,510,037.00 47,251,326.00 29,047,254.00 78667051 156,057,796.00

DANGOTE CEMENT 135,621,674.00 40,686,502.20 176,308,176.20 26,624,785.00 17,960,110.00 27,666,821.50 84556644 148,641,354.70

DANGOTE CEMENT 130,518,631.00 39,155,589.30 169,674,220.30 12,252,875.00 11,622,109.00 26,625,800.72 103837591 143,048,419.58

DN MEYER PLC. 1,908,874.00 572,662.20 2,481,536.20 231,935.00 236,374.00 879,522 981522 1,602,014.20

165

DN MEYER PLC. 2,001,752.00 600,525.60 2,602,277.60 473,237.00 627,069.00 838,026 838026 1,764,251.60

DN MEYER PLC. 2,075,759.00 622,727.70 2,698,486.70 297,580.00 296,417.00 448,278 448278 2,250,208.70

DN MEYER PLC. 765,378.00 229,613.40 994,991.40 83,326.00 63,778.00 61,757 61757 933,234.40

DN MEYER PLC. 260,923.00 78,276.90 339,199.90 47,315.00 60,753.00 46,183.37 196183.37 293,016.53

DN MEYER PLC. 251,238.00 75,371.40 326,609.40 207,154.00 208,793.00 44,469.13 44469.126 282,140.27

DN MEYER PLC. 274,705.00 82,411.50 357,116.50 90,134.00 62,680.00 48,622.79 48622.785 308,493.72

DN MEYER PLC. 276,983.00 83,094.90 360,077.90 117,236.00 66,796.00 49,025.99 49025.991 311,051.91

DN MEYER PLC. 272,539.00 81,761.70 354,300.70 110,389.00 75,333.00 48,239.40 48239.403 306,061.30

DN MEYER PLC. 155,082.00 46,524.60 201,606.60 103,647.00 72,138.00 27,449.51 27449.514 174,157.09

DN MEYER PLC. 150,923.00 45,276.90 196,199.90 57,816.00 45,743.00 26,713.37 26713.371 169,486.53

DN MEYER PLC. 146,265.00 43,879.50 190,144.50 41,836.00 38,624.00 25,888.91 25888.905 164,255.60

DN MEYER PLC. 163,471.00 49,041.30 212,512.30 98,223.00 92,764.00 28,934.37 28934.367 183,577.93

DN MEYER PLC. 151,228.00 45,368.40 196,596.40 24,157.00 19,359.00 26,767.36 41179.356 169,829.04

FIRST ALUMINUM NIGERIA PLC

6,274,980.00 1,882,494.00 8,157,474.00 298,070.00 334,586.00 1,110,671.46 1483314.5 7,046,802.54

FIRST ALUMINUM NIGERIA PLC

6,271,916.00 1,881,574.80 8,153,490.80 59,621.00 48,316.00 1,110,129.13 1349388.1 7,043,361.67

FIRST ALUMINUM NIGERIA PLC

2,183,308.00 654,992.40 2,838,300.40 473,092.00 298,652.00 386,445.52 592845.52 2,451,854.88

FIRST ALUMINUM NIGERIA PLC

2,299,991.00 689,997.30 2,989,988.30 583,106.00 491,584.00 407,098.41 1914217.4 2,582,889.89

FIRST ALUMINUM NIGERIA PLC

2,381,994.00 714,598.20 3,096,592.20 308,951.00 5,462.00 421,612.94 882301.94 2,674,979.26

FIRST ALUMINUM NIGERIA PLC

1,877,211.00 563,163.30 2,440,374.30 195,831.00 159,055.00 332,266.35 522414.35 2,108,107.95

FIRST ALUMINUM NIGERIA PLC

1,825,678.00 547,703.40 2,373,381.40 129,543.00 92,316.00 323,145.01 616867.01 2,050,236.39

FIRST ALUMINUM NIGERIA PLC

1,457,428.00 437,228.40 1,894,656.40 233,436.00 190,657.00 257,964.76 257964.76 1,636,691.64

FIRST ALUMINUM NIGERIA PLC

1,534,006.00 460,201.80 1,994,207.80 274,434.00 217,987.00 271,519.06 407080.06 1,722,688.74

FIRST ALUMINUM NIGERIA PLC

1,562,236.00 468,670.80 2,030,906.80 180,558.00 159,218.00 276,515.77 601206.77 1,754,391.03

FIRST ALUMINUM NIGERIA PLC

948,480.00 284,544.00 1,233,024.00 91,334.00 61,586.00 167,880.96 211102.96 1,065,143.04

166

FIRST ALUMINUM NIGERIA PLC

715,039.00 214,511.70 929,550.70 179,338.00 116,689.00 126,561.90 140999.9 802,988.80

FIRST ALUMINUM NIGERIA PLC

668,493.00 200,547.90 869,040.90 141,101.00 79,395.00 118,323.26 164735.26 750,717.64

FIRST ALUMINUM NIGERIA PLC

661,493.00 198,447.90 859,940.90 116,279.00 82,940.00 117,084.26 190807.26 742,856.64

IPWA PLC 178,699.00 53,609.70 232,308.70 77,752.00 59,675.00 31,629.72 31629.723 200,678.98

IPWA PLC 21,852.00 6,555.60 28,407.60 10,867.00 4,438.00 3,867.80 3867.804 24,539.80

IPWA PLC 244,635.00 73,390.50 318,025.50 29,363.00 21,510.00 43,300.40 43300.395 274,725.11

IPWA PLC 243,168.00 72,950.40 316,118.40 73,611.00 68,518.00 43,040.74 43040.736 273,077.66

IPWA PLC 250,729.00 75,218.70 325,947.70 49,230.00 50,187.00 179,635 179635 146,312.70

IPWA PLC 387,479.00 116,243.70 503,722.70 33.23 34,132.00 266.198 266.198 503,456.50

IPWA PLC 413,545.00 124,063.50 537,608.50 34,448.00 34,848.00 248,120 248120 289,488.50

IPWA PLC 446,491.00 133,947.30 580,438.30 76,418.00 77,403.00 285,382 285382 295,056.30

IPWA PLC 478,085.00 143,425.50 621,510.50 113,868.00 115,582.00 365,204 365204 256,306.50

IPWA PLC 563,450.00 169,035.00 732,485.00 122340 121,705.20 121705.2 610,779.80

IPWA PLC 182,631.00 54,789.30 237,420.30 140,485.00 140,819.00 13,559,591 13605973 -13,322,170.70

IPWA PLC 181,969.00 54,590.70 236,559.70 40,058.00 40,058.00 40,491 40491 196,068.70

IPWA PLC 185,898.00 55,769.40 241,667.40 14,555.00 10,907.00 4,363 4363 237,304.40

IPWA PLC 185,474.00 55,642.20 241,116.20 12,236.00 9,151.00 4,330 4330 236,786.20

IPWA PLC 189,771.00 56,931.30 246,702.30 11,548.00 9,813.00 9,134 9134 237,568.30

LAFARGE CEMENT WAPCO NIGERIA PLC

100,751,762.00 30,225,528.60 130,977,290.60 8,464,365.00 48,881,365.00 40,401,126 40401126 90,576,164.60

LAFARGE CEMENT WAPCO NIGERIA PLC

69,680,808.00 20,904,242.40 90,585,050.40 9,237.33 5,055,398.00 14,214,884.83 14214885 76,370,165.57

LAFARGE CEMENT WAPCO NIGERIA PLC

43,121,092.00 12,936,327.60 56,057,419.60 13,033,219.00 11,252,030.00 8,796,702.77 8796702.8 47,260,716.83

LAFARGE CEMENT WAPCO NIGERIA PLC

33,356,068.00 10,006,820.40 43,362,888.40 12,536,131.00 10,678,65 6,804,637.87 6804637.9 36,558,250.53

LAFARGE CEMENT WAPCO NIGERIA PLC

32,361,135.00 9,708,340.50 42,069,475.50 12,119,592.00 10,946,204.00 68,020 68020 42,001,455.50

PAINTS & COATING MANUFACTURES

NIGERIA PLC

155,055.00 46,516.50 201,571.50 108,607.00 106,669.00 627,475 627475 -425,903.50

167

PAINTS & COATING MANUFACTURES

NIGERIA PLC

160,693.00 48,207.90 208,900.90 29,184.00 17,804.00 188,703 188703 20,197.90

PAINTS & COATING MANUFACTURES

NIGERIA PLC

4,882.00 1,464.60 6,346.60 109,274.00 76,492.00 273,320 273320 -266,973.40

PAINTS & COATING MANUFACTURES

NIGERIA PLC

5,301.00 1,590.30 6,891.30 68,873.00 45,222.00 358,437 358437 -351,545.70

PAINTS & COATING MANUFACTURES

NIGERIA PLC

13,049.00 3,914.70 16,963.70 51,767.00 33,767.00 256,321 300699 -239,357.30

VITAFOAM 1,692,426.00 507,727.80 2,200,153.80 823,252.00 514,170.00 3,127,623 3683623 -927,469.20

VITAFOAM 1,651,125.00 495,337.50 2,146,462.50 780,915.00 512,532.00 2,734,683 2734683 -588,220.50

VITAFOAM 1,342,847.00 402,854.10 1,745,701.10 1,013,719.00 698,296.00 2,101,498 2101498 -355,796.90

VITAFOAM 629,973.00 188,991.90 818,964.90 652,284.00 439,314.00 1,719,760 1719760 -900,795.10

VITAFOAM 783,456.00 235,036.80 1,018,492.80 564,300.00 452,000.00 157,474.66 157474.66 861,018.14

VITAFOAM 501,610.00 150,483.00 652,093.00 173,492.00 111,647.00 822,355 822355 -170,262.00

VITAFOAM 559,456.00 167,836.80 727,292.80 402,234.00 272,234.00 946,208 946208 -218,915.20

VITAFOAM 589,963.00 176,988.90 766,951.90 485,659.00 306,859.00 1,519,539 1519539 -752,587.10

VITAFOAM 563,577.00 169,073.10 732,650.10 413,601.00 258,401.00 1,024,679 1024679 -292,028.90

VITAFOAM 454,848.00 136,454.40 591,302.40 396,781.00 257,281.00 842,254 842254 -250,951.60

VITAFOAM 348,418.00 104,525.40 452,943.40 240,239.00 151,081.00 742,653 742653 -289,709.60

VITAFOAM 289,573, #VALUE! 342700 204,897.00 134,397.00 256,611 256611 117650

VITAFOAM 174,264.00 52,279.20 226,543.20 169,089.00 105,810.00 122.134 122.134 226,421.07

VITAFOAM 102,108.00 30,632.40 132,740.40 143,364.00 102,062.00 280,598 280598 -147,857.60

VONO PRODUCT 1,724,223.00 517,266.90 2,241,489.90 393,350.00 396,974.00 665,833 665833 1,575,656.90

VONO PRODUCT 1,801,698.00 540,509.40 2,342,207.40 247,983.00 253,597.00 609,671 609671 1,732,536.40

VONO PRODUCT 742,818.00 222,845.40 965,663.40 118,647.00 120,166.00 306,959 306959 658,704.40

VONO PRODUCT 781,062.00 234,318.60 1,015,380.60 545,070.00 548,142.00 487,493 487493 527,887.60

VONO PRODUCT 223,604.00 67,081.20 290,685.20 3,522.00 1,134.00 48,298.46 48298.464 242,386.74

168

VONO PRODUCT 204,863.00 61,458.90 266,321.90 87,995.00 77,995.00 44,250.41 44250.408 222,071.49

VONO PRODUCT 126,437.00 37,931.10 164,368.10 200,857.00 188,862.00 27,310.39 27310.392 137,057.71

VONO PRODUCT 170,554.00 51,166.20 221,720.20 18,589.00 15,889.00 36,839.66 36839.664 184,880.54

VONO PRODUCT 186,413.00 55,923.90 242,336.90 23,350.00 15,072.00 40,265.21 40265.208 202,071.69

VONO PRODUCT 170,873.00 51,261.90 222,134.90 3,847.00 1,747.00 36,908.57 36908.568 185,226.33

VONO PRODUCT 130,552.00 39,165.60 169,717.60 15,845.00 11,987.00 28,199.23 28199.232 141,518.37

VONO PRODUCT 179,020.00 53,706.00 232,726.00 10,810.00 8,986.00 38,668.32 38668.32 194,057.68

VONO PRODUCT 177,937.00 53,381.10 231,318.10 62,286.00 63,176.00 38,434.39 38434.392 192,883.71

VONO PRODUCT 188,592.00 56,577.60 245,169.60 15,915.00 12,042.00 40,735.87 40735.872 204,433.73

PZ CUSSONS 24,737,693.00 7,421,307.90 32,159,000.90 7,951,448.00 5,301,742.00 5,343,341.69 5343341.7 26,815,659.21

PZ CUSSONS 21,511,819.00 6,453,545.70 27,965,364.70 7,671,087.00 4,818,611.00 4,646,552.90 4646552.9 23,318,811.80

PZ CUSSONS 18,143,134.00 5,442,940.20 23,586,074.20 5,980,297.00 3,950,935.00 3,918,916.94 3918916.9 19,667,157.26

PZ CUSSONS 16,366,285.00 4,909,885.50 21,276,170.50 5,355,884.00 3,572,346.00 3,535,117.56 3535117.6 17,741,052.94

PZ CUSSONS 14,369,900.00 4,310,970.00 18,680,870.00 4,803,708.00 3,235,587.00 3,103,898.40 3103898.4 15,576,971.60

PZ CUSSONS 11,985,529.00 3,595,658.70 15,581,187.70 4,379,952.00 3,237,173.00 2,588,874.26 2588874.3 12,992,313.44

PZ CUSSONS 11,820,528.00 3,546,158.40 15,366,686.40 3,303,662.00 3,303,662.00 2,553,234.05 2553234 12,813,452.35

PZ CUSSONS 8,341,636.00 2,502,490.80 10,844,126.80 2,859,678.00 2,010,846.00 1,801,793.38 1801793.4 9,042,333.42

PZ CUSSONS 7,155,344.00 2,146,603.20 9,301,947.20 2,430,740.00 1,685,918.00 1,545,554.30 1545554.3 7,756,392.90

PZ CUSSONS 6,537,084.00 1,961,125.20 8,498,209.20 1,787,083.00 1,270,157.00 1,412,010.14 1412010.1 7,086,199.06

PZ CUSSONS 6,732,488.00 2,019,746.40 8,752,234.40 1,352,686.00 932,288.00 1,454,217.41 1454217.4 7,298,016.99

PZ CUSSONS 6,897,392.00 2,069,217.60 8,966,609.60 177283 851,646.00 1,489,836.67 1489836.7 7,476,772.93

PZ CUSSONS 13,662,393.00 4,098,717.90 17,761,110.90 1,615,157.00 1,147,137.00 2,951,076.89 2951076.9 14,810,034.01

PZ CUSSONS 3,161,579.00 948,473.70 4,110,052.70 2,181,370.00 1,489,992.00 682,901.06 682901.06 3,427,151.64

UNILEVER 11,739,578.00 3,521,873.40 15,261,451.40 6,151,855.00 4,180,620.00 199,410 3404351 15,062,041.40

UNILEVER 9,975,242.00 2,992,572.60 12,967,814.60 5,661,052.00 4,093,822.00 2,005,023.64 5079359.6 10,962,790.96

UNILEVER 9,056,190.00 2,716,857.00 11,773,047.00 4,144,849.00 2,596,533.00 1,820,294.19 4888679.2 9,952,752.81

169

UNILEVER 8,640,971.00 2,592,291.30 11,233,262.30 2,013,148.00 1,296,533.00 1,029,428 3610127 10,203,834.30

UNILEVER 7,772,471.00 2,331,741.30 10,104,212.30 2,120,233.00 1,374,363.00 1,260,776 3819123 8,843,436.30

UNILEVER 7,645,186.00 2,293,555.80 9,938,741.80 2,281,416.00 1,616,457.00 1,651,360.18 4578924.2 8,287,381.62

UNILEVER 6,179,653.00 1,853,895.90 8,033,548.90 2,970,047.00 2,164,249.00 1,334,805.05 3424266 6,698,743.85

UNILEVER 4,822,861.00 1,446,858.30 6,269,719.30 2,778,115.00 1,870,258.00 1,041,737.98 2754781 5,227,981.32

UNILEVER 4,498,208.00 1,349,462.40 5,847,670.40 2,053,089.00 1,571,918.00 971,612.93 2194309.9 4,876,057.47

UNILEVER 3,598,035.00 1,079,410.50 4,677,445.50 1,585,738.00 2,164,114.00 777,175.56 1762019.6 3,900,269.94

UNILEVER 2,934,686.00 880,405.80 3,815,091.80 1,294,780.00 853,992.00 633,892.18 633892.18 3,181,199.62

UNILEVER 2,615,223.00 784,566.90 3,399,789.90 594,046.00 437,853.00 564,888.17 564888.17 2,834,901.73

UNILEVER 2,440,618.00 732,185.40 3,172,803.40 282,383.00 136,737.00 527,173.49 527173.49 2,645,629.91

UNILEVER 2,586,598.00 775,979.40 3,362,577.40 92,223.00 132,223.00 558,705.17 558705.17 2,803,872.23

EKOCORP PLC 1,181,519,752.00

354,455,925.60 1,535,975,677.60 40,441,256.00 29,179,103.00 255,208,266.43 255208266 1,280,767,411.17

EKOCORP PLC 1,233,885,574.00

370,165,672.20 1,604,051,246.20 41,217,588.00 31,543,699.00 266,519,283.98 266519284 1,337,531,962.22

EKOCORP PLC 1,126,942,051.00

338,082,615.30 1,465,024,666.30 71,857,493.00 57,885,279.00 243,419,483.02 243419483 1,221,605,183.28

EKOCORP PLC 1,056,306,832.00

316,892,049.60 1,373,198,881.60 87,083,833.00 72,157,871.00 157,985,157 157985157 1,215,213,724.60

EKOCORP PLC 1,168,795,640.00

350,638,692.00 1,519,434,332.00 83,314,106.00 65,610,423.00 294,842,440 294842440 1,224,591,892.00

EKOCORP PLC 559,186,225.00 167,755,867.50 726,942,092.50 74,183,915.00 62,319,326.00 92,265,727.13 92265727 634,676,365.38

EKOCORP PLC 445,174,641.00 133,552,392.30 578,727,033.30 65,575,610.00 57,599,311.00 73,453,815.77 73453816 505,273,217.54

EKOCORP PLC 457,299,440.00 137,189,832.00 594,489,272.00 60,763,340.00 55,009,374.00 75,454,407.60 75454408 519,034,864.40

EKOCORP PLC 326,666,534.00 97,999,960.20 424,666,494.20 58,493,838.00 51,634,534.00 53,899,978.11 53899978 370,766,516.09

EKOCORP PLC 337,143,234.00 101,142,970.20 438,286,204.20 52,513,615.00 46,242,639.00 55,628,633.61 55628634 382,657,570.59

EKOCORP PLC 452,370,850.00 135,711,255.00 588,082,105.00 56,320,000.00 47,008,900.00 74,641,190.25 74641190 513,440,914.75

EKOCORP PLC 546,389,000.00 163,916,700.00 710,305,700.00 42,667,198.00 36,078,466.00 90,154,185.00 90154185 620,151,515.00

EKOCORP PLC 345,100,195.00 103,530,058.50 448,630,253.50 41,283,555.00 34,865,289.00 56,941,532.18 56941532 391,688,721.33

EKOCORP PLC 307,094,509.00 92,128,352.70 399,222,861.70 34,796,608.00 29,377,259.00 50,670,593.99 50670594 348,552,267.72

170

EKOCORP PLC 134,582,021.00 40,374,606.30 174,956,627.30 50,866,095.00 38,757,607.00 22,206,033.47 22206033 152,750,593.84

EKOCORP PLC 124,608,521.00 37,382,556.30 161,991,077.30 44,907,310.00 32,390,899.00 20,560,405.97 20560406 141,430,671.34

UNION DIAGONISTIC AND CLINICAL

SERVICES

1,514,009.00 454,202.70 1,968,211.70 213,302.00 158,909.00 249,811.49 249811.49 1,718,400.22

UNION DIAGONISTIC AND CLINICAL

SERVICES

1,796,448.00 538,934.40 2,335,382.40 188,747.00 110,353.00 296,413.92 296413.92 2,038,968.48

UNION DIAGONISTIC AND CLINICAL

SERVICES

1,745,992.00 523,797.60 2,269,789.60 709,889.00 370,089.00 288,088.68 288088.68 1,981,700.92

UNION DIAGONISTIC AND CLINICAL

SERVICES

175,910.00 52,773.00 228,683.00 314,566.00 255,078.00 29,025.15 29025.15 199,657.85

UNION DIAGONISTIC AND CLINICAL

SERVICES

203,929.00 61,178.70 265,107.70 64,375.00 59,368.00 33,648.29 33648.285 231,459.42

EVANS MEDICAL 1,615,632.00 484,689.60 2,100,321.60 54,379.00 8,763.00 2,401,959 4150242 -301,637.40

EVANS MEDICAL 1,683,694.00 505,108.20 2,188,802.20 658,983.00 889,591.00 3,746,540 4032953 -1,557,737.80

EVANS MEDICAL 1,735,509.00 520,652.70 2,256,161.70 387,824.00 510,098.00 3,514,758 3515116.7 -1,258,596.30

EVANS MEDICAL 1,598,611.00 479,583.30 2,078,194.30 373,436.00 317,019.00 2,823,171 3023873 -744,976.70

EVANS MEDICAL 1,404,258.00 421,277.40 1,825,535.40 186,613.00 132,204.00 2,023,317 2178476 -197,781.60

EVANS MEDICAL 1,340,000.00 402,000.00 1,742,000.00 194,560.00 154,890.00 172,860.00 172860 1,569,140.00

EVANS MEDICAL 1,117,037.00 335,111.10 1,452,148.10 58,265.00 12,676.00 144,097.77 144097.77 1,308,050.33

EVANS MEDICAL 1,060,985.00 318,295.50 1,379,280.50 36,920.00 89,033.00 136,867.07 136867.07 1,242,413.44

EVANS MEDICAL 1,093,612.00 328,083.60 1,421,695.60 27,845.00 97,953.00 18.031 18.031 1,421,677.57

EVANS MEDICAL 1,122,315.00 336,694.50 1,459,009.50 25,527.00 60,122.00 129,869 129869 1,329,140.50

EVANS MEDICAL 1,151,700.00 345,510.00 1,497,210.00 52,667.00 48,064.00 199,522 199522 1,297,688.00

EVANS MEDICAL 165,368.00 49,610.40 214,978.40 67,508.00 59,974.00 252,524 252524 -37,545.60

EVANS MEDICAL 180,504.00 54,151.20 234,655.20 242,423.00 242,423.00 207,524 207524 27,131.20

EVANS MEDICAL 175,800.00 52,740.00 228,540.00 2,112.00 9,166.00 39,529 39994 189,011.00

EVANS MEDICAL 132,300.00 39,690.00 171,990.00 37,505.00 30,452.00 94,321 96786 77,669.00

171

MORRISON INDUSTRIES

368,581.00 110,574.30 479,155.30 33,682.00 33,127.00 125,381 125381 353,774.30

MORRISON INDUSTRIES

394,466.00 118,339.80 512,805.80 20,452.00 20,857.00 126,144 126144 386,661.80

MORRISON INDUSTRIES

412,139.00 123,641.70 535,780.70 20,165.00 14,449.00 94,522 94522 441,258.70

MORRISON INDUSTRIES

67,858.00 20,357.40 88,215.40 536 5,490.00 82,186 82186 6,029.40

MORRISON INDUSTRIES

66,542.00 19,962.60 86,504.60 14,282.00 8,147.00 75,944 75944 10,560.60

MORRISON INDUSTRIES

55,900.00 16,770.00 72,670.00 13,540.00 33,127.00 5,701.80 5701.8 66,968.20

MORRISON INDUSTRIES

37,804.00 11,341.20 49,145.20 19,695.00 9,667.00 53,838 53838 -4,692.80

MORRISON INDUSTRIES

33,476.00 10,042.80 43,518.80 16,037.00 9,521.00 27,704 27704 15,814.80

MORRISON INDUSTRIES

35,092.00 10,527.60 45,619.60 10,991.00 6,341.00 24,897 24897 20,722.60

MORRISON INDUSTRIES

41,137.00 12,341.10 53,478.10 6,173.00 11,591.00 28,402 28402 25,076.10

MORRISON INDUSTRIES

44,319.00 13,295.70 57,614.70 3,380.00 3,183.00 12,114 12114 45,500.70

MORRISON INDUSTRIES

37,549.00 11,264.70 48,813.70 9,310.00 7,031.00 12,122 12122 36,691.70

MORRISON INDUSTRIES

27,425.00 8,227.50 35,652.50 7,839.00 7,839.00 20,318 20318 15,334.50

MORRISON INDUSTRIES

13,062.00 3,918.60 16,980.60 9,884.00 9,884.00 27,174 27174 -10,193.40

FIDSON HEALTH CARE

2,150,631.00 645,189.30 2,795,820.30 465,893.00 465,893.00 643,124 643124 2,152,696.30

FIDSON HEALTH CARE

1,903,839.00 571,151.70 2,474,990.70 429,073.00 429,073.00 623,036 623036 1,851,954.70

FIDSON HEALTH CARE

870,478.00 261,143.40 1,131,621.40 189,300.00 189,300.00 526,379 526379 605,242.40

FIDSON HEALTH CARE

586,482.00 175,944.60 762,426.60 505,304.00 505,304.00 505,304 505304 257,122.60

FIDSON HEALTH CARE

501,154.00 150,346.20 651,500.20 370,430.00 370,430.00 370,430 370430 281,070.20

PHARMA DECO 571,778.00 171,533.40 743,311.40 462,919.00 464,094.00 1,014,648 1714648 -271,336.60

PHARMA DECO 584,509.00 175,352.70 759,861.70 460,455.00 461,497.00 1,301,547 1301547 -541,685.30

PHARMA DECO 622,556.00 186,766.80 809,322.80 194,826.00 197,972.00 915,904 915904 -106,581.20

172

PHARMA DECO 652,283.00 195,684.90 847,967.90 239,801.00 242,284.00 787,353 787353 60,614.90

PHARMA DECO 616,516.00 184,954.80 801,470.80 357,559.00 337,330.00 586,914 586914 214,556.80

PHARMA DECO 498,663.00 149,598.90 648,261.90 12,088.00 8,216.00 14,999 14999 633,262.90

PHARMA DECO 368,499.00 110,549.70 479,048.70 36,970.00 30,619.00 37,586.90 37586.898 441,461.80

PHARMA DECO 213,947.00 64,184.10 278,131.10 69,939.00 63,598.00 58,585 58585 219,546.10

PHARMA DECO 161,732.00 48,519.60 210,251.60 50,455.00 42,304.00 1,164,470.40 1164470.4 -954,218.80

PHARMA DECO 167,120.00 50,136.00 217,256.00 5,732.00 4,806.00 1,203,264.00 1203264 -986,008.00

PHARMA DECO 169,000.00 50,700.00 219,700.00 198345 66,833.00 1,216,800.00 1216800 -997,100.00

PHARMA DECO 166,309.00 49,892.70 216,201.70 94,657.00 95,465.00 28,910 36594 187,291.70

PHARMA DECO 170,788.00 51,236.40 222,024.40 34,598.00 35,976.00 16,908.01 27725.012 205,116.39

PHARMA DECO 168,850.00 50,655.00 219,505.00 14,612.00 14,612.00 16,716.15 33872.15 202,788.85

PHARMA DECO 171,733.00 51,519.90 223,252.90 11,837.00 11,837.00 17,001.57 274748.57 206,251.33

PHARMA DECO 15,671.00 4,701.30 20,372.30 25,142.00 25,142.00 1,551.43 65334.429 18,820.87

ASHAKA CEMENT 18,701,082.00 5,610,324.60 24,311,406.60 4,389,168.00 4,004,694.00 1,851,407.12 1914352.1 22,459,999.48

ASHAKA CEMENT 19,066,089.00 5,719,826.70 24,785,915.70 2,365,777.00 943,618.00 3,095,860 3095860 21,690,055.70

ASHAKA CEMENT 16,587,718.00 4,976,315.40 21,564,033.40 3,420,941.00 2,070,045.00 1,676.89 1676.885 21,562,356.52

ASHAKA CEMENT 12,702,177.00 3,810,653.10 16,512,830.10 2,514,625.00 1,603,456.00 443,440 460347 16,069,390.10

ASHAKA CEMENT 8,018,713.00 2,405,613.90 10,424,326.90 4,951,464.00 3,377,481.00 625,459.61 657450.61 9,798,867.29

ASHAKA CEMENT 4,508,778.00 1,352,633.40 5,861,411.40 6,519,249.00 4,429,884.00 351,684.68 351684.68 5,509,726.72

ASHAKA CEMENT 2,499,175.00 749,752.50 3,248,927.50 4,892,887.00 3,380,667.00 194,935.65 194935.65 3,053,991.85

ASHAKA CEMENT 1,875,533.00 562,659.90 2,438,192.90 3,135,497.00 2,123,170.00 146,291.57 146291.57 2,291,901.33

ASHAKA CEMENT 1,534,639.00 460,391.70 1,995,030.70 2,093,071.00 1,522,289.00 119,701.84 119701.84 1,875,328.86

ASHAKA CEMENT 1,512,229.00 453,668.70 1,965,897.70 2,792,578.00 1,850,970.00 117,953.86 117953.86 1,847,943.84

ASHAKA CEMENT 1,152,358.00 345,707.40 1,498,065.40 1,334,592.00 861,862.00 89,883.92 89883.924 1,408,181.48

ASHAKA CEMENT 867,859.00 260,357.70 1,128,216.70 882,330.00 565,923.00 67,693.00 67693.002 1,060,523.70

ASHAKA CEMENT 798,845.00 239,653.50 1,038,498.50 551,688.00 393,682.00 62,309.91 62309.91 976,188.59

173

ASHAKA CEMENT 812,609.00 243,782.70 1,056,391.70 916,983.00 631,172.00 63,383.50 68277.502 993,008.20

AFRICAN PAINT 348,383.00 104,514.90 452,897.90 13,999.00 14,389.00 8,739 8739 444,158.90

AFRICAN PAINT 360,184.00 108,055.20 468,239.20 31,144.00 31,622.00 17,508 17508 450,731.20

AFRICAN PAINT 366,859.00 110,057.70 476,916.70 61,505.00 61,806.00 9,131 9131 467,785.70

AFRICAN PAINT 276,245.00 82,873.50 359,118.50 16,189.00 16,426.00 16,466 20165 342,652.50

AFRICAN PAINT 289,747.00 86,924.10 376,671.10 22,040.00 22,299.00 29,978 43677 346,693.10

BERGER PAINTS 1,052,639.00 315,791.70 1,368,430.70 519,897.00 442,463.00 1,815,912 1815912 -447,481.30

BERGER PAINTS 1,060,862.00 318,258.60 1,379,120.60 322,867.00 193,276.00 1,547,562 1547562 -168,441.40

BERGER PAINTS 1,093,003.00 327,900.90 1,420,903.90 244,828.00 148,740.00 1,407,058 1407058 13,845.90

BERGER PAINTS 1,290.03 387.01 1,677.03 211,907.00 112,619.00 1,264,638 1264638 -1,262,960.97

BERGER PAINTS 1,251,048.00 375,314.40 1,626,362.40 110,386.00 81,678.00 1,145,444 1145444 480,918.40

BERGER PAINTS 1,278,937.00 383,681.10 1,662,618.10 -68,346.00 -44,906.00 1,055,529 1055529 607,089.10

BERGER PAINTS 1,278,571.00 383,571.30 1,662,142.30 166,411.00 101,542.00 605,310 605310 1,056,832.30

BERGER PAINTS 235,573.00 70,671.90 306,244.90 168,021.00 108,534.00 565,562 565562 -259,317.10

BERGER PAINTS 250,502.00 75,150.60 325,652.60 130,835.00 85,941.00 521,968 521968 -196,315.40

BERGER PAINTS 213,166.00 63,949.80 277,115.80 135,921.00 88,548.00 464,938 464938 -187,822.20

BERGER PAINTS 182,243.00 54,672.90 236,915.90 37,879.00 19,947.00 420,958 420958 -184,042.10

BERGER PAINTS 144,861.00 43,458.30 188,319.30 54,513.00 34,940.00 419,871 419871 -231,551.70

BERGER PAINTS 146,292.00 43,887.60 190,179.60 52,595.00 40.1 223,003 223003 -32,823.40

BERGER PAINTS 69,662.00 20,898.60 90,560.60 93,492.00 73,993.00 218,371 218371 -127,810.40

CHEMICAL AND ALLIED

247,875.00 74,362.50 322,237.50 1,139,014.00 882,856.00 17,103.38 17103.375 305,134.13

CHEMICAL AND ALLIED

245,154.00 73,546.20 318,700.20 619,297.00 340,981.00 16,915.63 16915.626 301,784.57

CHEMICAL AND ALLIED

236,974.00 71,092.20 308,066.20 997,276.00 735,642.00 16,351.21 16351.206 291,714.99

CHEMICAL AND ALLIED

144,764.00 43,429.20 188,193.20 566,688.00 351,528.00 9,988.72 9988.716 178,204.48

CHEMICAL AND ALLIED

172,347.00 51,704.10 224,051.10 456,400.00 312,748.00 11,891.94 11891.943 212,159.16

174

CHEMICAL AND ALLIED

161,168.00 48,350.40 209,518.40 302,660.00 201,571.00 11,120.59 11120.592 198,397.81

CHEMICAL AND ALLIED

177,747.00 53,324.10 231,071.10 250,842.00 161,455.00 12,264.54 12264.543 218,806.56

CHEMICAL AND ALLIED

146,498.00 43,949.40 190,447.40 208,634.00 151,782.00 10,108.36 10108.362 180,339.04

CHEMICAL AND ALLIED

127,766.00 38,329.80 166,095.80 178,973.00 140,806.00 8,815.85 8815.854 157,279.95

CHEMICAL AND ALLIED

71,291.00 21,387.30 92,678.30 411,608.00 400,457.00 4,919.08 4919.079 87,759.22

CHEMICAL AND ALLIED

62,375.00 18,712.50 81,087.50 15,019.00 8,809.00 4,303.88 4303.875 76,783.63

CHEMICAL AND ALLIED

82,150.00 24,645.00 106,795.00 145,257.00 94,448.00 9,935 9935 96,860.00

CHEMICAL AND ALLIED

93,461.00 28,038.30 121,499.30 30,597.00 17,348.00 6,448.81 6448.809 115,050.49

CHEMICAL AND ALLIED

105,284.00 31,585.20 136,869.20 12,256.00 70,773.00 7,264.60 7264.596 129,604.60

CEMENT COMPANY OF NORTHERN

NIGERIA

285,442,982.00 85,632,894.60 371,075,876.60 1,752,034.00 106,605,409.00 4,377.57 98255791 371,071,499.03

CEMENT COMPANY OF NORTHERN

NIGERIA

186,393,346.00 55,918,003.80 242,311,349.80 2,317,300.00 61,392,230.00 4,630.91 49624428 242,306,718.90

CEMENT COMPANY OF NORTHERN

NIGERIA

142,388,500.00 42,716,550.00 185,105,050.00 1,680,524.00 47,257,326.00 5,574,628 55194425 179,530,422.00

CEMENT COMPANY OF NORTHERN

NIGERIA

135,621,674.00 40,686,502.20 176,308,176.20 172,848.00 17,960,110.00 6,155,661 63045543 170,152,515.20

CEMENT COMPANY OF NORTHERN

NIGERIA

130,518,631.00 39,155,589.30 169,674,220.30 10,443.00 11,622,109.00 4,328,601 81540391 165,345,619.30

CEMENT COMPANY OF NORTHERN

NIGERIA

2,140,175.00 642,052.50 2,782,227.50 379,886.00 22,282.00 3,508,387 3508387 -726,159.50

CEMENT COMPANY OF NORTHERN

NIGERIA

2,160,468.00 648,140.40 2,808,608.40 845,081.00 827,081.00 2,648,768 2648768 159,840.40

CEMENT COMPANY OF NORTHERN

NIGERIA

2,074,289.00 622,286.70 2,696,575.70 93,351.00 108,351.00 2,067,220 2067220 629,355.70

CEMENT COMPANY OF NORTHERN

1,062,659.00 318,797.70 1,381,456.70 568,381.00 568,381.00 1,329,414 1329414 52,042.70

175

NIGERIA

CEMENT COMPANY OF NORTHERN

NIGERIA

917,617.00 275,285.10 1,192,902.10 1,064,273.00 1,074,494.00 588,434 588434 604,468.10

CEMENT COMPANY OF NORTHERN

NIGERIA

633,081.00 189,924.30 823,005.30 488,778.00 508,961.00 427,820 427820 395,185.30

CEMENT COMPANY OF NORTHERN

NIGERIA

663,493.00 199,047.90 862,540.90 12,409.00 6,416.00 394,584 394584 467,956.90

CEMENT COMPANY OF NORTHERN

NIGERIA

695,168.00 208,550.40 903,718.40 11,678.00 6,936.00 313,176 313176 590,542.40

CEMENT COMPANY OF NORTHERN

NIGERIA

713,803.00 214,140.90 927,943.90 14,205.00 3,468.00 49,252.41 49252.407 878,691.49

UTC NIGERIA 2,029,269.00 608,780.70 2,638,049.70 5,201.00 79,802.00 448,237 448237 2,189,812.70

UTC NIGERIA 1,997,431.00 599,229.30 2,596,660.30 76,774.00 74,768.00 387,279 387279 2,209,381.30

UTC NIGERIA 2,033,665.00 610,099.50 2,643,764.50 49,388.00 46,362.00 545,187 545187 2,098,577.50

UTC NIGERIA 1,908,225.00 572,467.50 2,480,692.50 40,168.00 37,565.00 430,027 430027 2,050,665.50

UTC NIGERIA 879,438.00 263,831.40 1,143,269.40 54,318.00 52,561.00 129,184 129184 1,014,085.40

UTC NIGERIA 445,167.00 133,550.10 578,717.10 286,552.00 166,736.00 42,736.03 42736.032 535,981.07

UTC NIGERIA 1,318,152.00 395,445.60 1,713,597.60 290,042.00 74,115.00 126,542.59 126542.59 1,587,055.01

UTC NIGERIA 2,180,804.00 654,241.20 2,835,045.20 319,622.00 350,292.00 209,357.18 209357.18 2,625,688.02

UTC NIGERIA 2,125,283.00 637,584.90 2,762,867.90 317,341.00 370,565.00 204,027.17 204027.17 2,558,840.73

UTC NIGERIA 2,614,321.00 784,296.30 3,398,617.30 157,165.00 116,236.00 1,250,477 1250477 2,148,140.30

UTC NIGERIA 2,499,212.00 749,763.60 3,248,975.60 64,390.00 54,907.00 1,532,737 1532737 1,716,238.60

UTC NIGERIA 867,487.00 260,246.10 1,127,733.10 446,084.00 448,379.00 2,148,552 2148552 -1,020,818.90

UTC NIGERIA 820,054.00 246,016.20 1,066,070.20 924,797.00 930,420.00 1,766,990 1766990 -700,919.80

UTC NIGERIA 1,150,449.00 345,134.70 1,495,583.70 609,462.00 612,167.00 971,585 971585 523,998.70

UNION DICON SALT 1,167,900.00 350,370.00 1,518,270.00 223,089.00 220,567.00 80,585.10 80585.1 1,437,684.90

UNION DICON SALT 1,567,980.00 470,394.00 2,038,374.00 198,456.00 189,003.00 108,190.62 108190.62 1,930,183.38

176

UNION DICON SALT 1,789,300.00 536,790.00 2,326,090.00 200,045.00 188,390.00 123,461.70 123461.7 2,202,628.30

UNION DICON SALT 77,640.00 23,292.00 100,932.00 202,864.00 203,154.00 819,103 819103 -718,171.00

UNION DICON SALT 119,222.00 35,766.60 154,988.60 188,174.00 188,464.00 797,956 797956 -642,967.40

UNION DICON SALT 183,426.00 55,027.80 238,453.80 141,751.00 142,180.00 784,592 784592 -546,138.20

UNION DICON SALT 264,258.00 79,277.40 343,535.40 481,607.00 482,226.00 702,283 702283 -358,747.60

UNION DICON SALT 341,486.00 102,445.80 443,931.80 604,922.00 374,967.00 778,133 778133 -334,201.20

UNION DICON SALT 453,890.00 136,167.00 590,057.00 789,430.00 734,500.00 31,318.41 31318.41 558,738.59

UNION DICON SALT 547,900.00 164,370.00 712,270.00 546,399.00 567,890.00 37,805.10 37805.1 674,464.90

UNION DICON SALT 623,852.00 187,155.60 811,007.60 244,962.00 211,417.00 43,045.79 43045.788 767,961.81

UNION DICON SALT 745,360.00 223,608.00 968,968.00 278,286.00 260,332.00 51,429.84 51429.84 917,538.16

UNION DICON SALT 543,890.00 163,167.00 707,057.00 325,133.00 299,293.00 37,528.41 37528.41 669,528.59

UNION DICON SALT 789,035.00 236,710.50 1,025,745.50 324,680.00 313,172.00 54,443.42 54443.415 971,302.09

UNION DICON SALT 852,900.00 255,870.00 1,108,770.00 363,099.00 363,028.00 58,850.10 58850.1 1,049,919.90

CADBURY NIGERIA 762,451.00 228,735.30 991,186.30 1,952,559.00 1,168,167.00 12,285,563 15382563 -11,294,376.70

CADBURY NIGERIA 834,560.00 250,368.00 1,084,928.00 23,794,400.00 91,235,917.00 9,011,945 12581691 -7,927,017.00

CADBURY NIGERIA 731,930.00 219,579.00 951,509.00 2,847,703.00 2,752,268.00 23,180,450 26913976 -22,228,941.00

CADBURY NIGERIA 12,345,567.00 3,703,670.10 16,049,237.10 94,197,948.00 726,978.00 21,201,171 24247795 -5,151,933.90

CADBURY NIGERIA 10,983,450.00 3,295,035.00 14,278,485.00 5,762,809.00 4,665,459.00 24,096,391 27477433 -9,817,906.00

CADBURY NIGERIA 7,664,693.00 2,299,407.90 9,964,100.90 3,853,094.00 2,710,921.00 781,798.69 781798.69 9,182,302.21

CADBURY NIGERIA 6,230,817.00 1,869,245.10 8,100,062.10 3,849,273.00 2,812,623.00 635,543.33 635543.33 7,464,518.77

CADBURY NIGERIA 6,345,678.00 1,903,703.40 8,249,381.40 3,792,506.00 236,869.00 647,259.16 647259.16 7,602,122.24

CADBURY NIGERIA 3,337,240.00 1,001,172.00 4,338,412.00 3,259,866.00 2,240,078.00 340,398.48 340398.48 3,998,013.52

CADBURY NIGERIA 2,245,052.00 673,515.60 2,918,567.60 2,405,720.00 1,647,836.00 228,995.30 228995.3 2,689,572.30

CADBURY NIGERIA 2,204,575.00 661,372.50 2,865,947.50 1,637,205.00 1,064,163.00 224,866.65 224866.65 2,641,080.85

CADBURY NIGERIA 1,970,921.00 591,276.30 2,562,197.30 1,236,913.00 751,740.00 201,033.94 201033.94 2,361,163.36

CADBURY NIGERIA 2,056,695.00 617,008.50 2,673,703.50 1,010,431.00 727,363.00 209,782.89 209782.89 2,463,920.61

177

CADBURY NIGERIA 1,822,254.00 546,676.20 2,368,930.20 940,536.00 709,282.00 185,869.91 185869.91 2,183,060.29

NESTLE NIGERIA 40,241,739.00 12,072,521.70 52,314,260.70 18,244,454.00 12,602,109.00 19,455,299 46081709 32,858,961.70

NESTLE NIGERIA 25,404,616.00 7,621,384.80 33,026,000.80 13,783,244.00 9,783,578.00 19,010,968 33706435 14,015,032.80

NESTLE NIGERIA 13,817,348.00 4,145,204.40 17,962,552.40 11,862,213.00 8,331,599.00 11,093,617 20128312 6,868,935.40

NESTLE NIGERIA 10,435,952.00 3,130,785.60 13,566,737.60 8,463,788.00 5,441,899.00 8,236,796 15015799 5,329,941.60

NESTLE NIGERIA 7,336,015.00 2,200,804.50 9,536,819.50 8,197,897.00 5,660,329.00 7,325,189 12547723 2,211,630.50

NESTLE NIGERIA 6,183,324.00 1,854,997.20 8,038,321.20 7,967,848.00 5,303,128.00 7,233,743 10894772 804,578.20

NESTLE NIGERIA 3,980,527.00 1,194,158.10 5,174,685.10 6,100,281.00 3,935,495.00 5,822,235 9023624 -647,549.90

NESTLE NIGERIA 2,124,548.00 637,364.40 2,761,912.40 5,846,923.00 3,804,114.00 5,362,854 7670201 -2,600,941.60

NESTLE NIGERIA 1,225,635.00 367,690.50 1,593,325.50 4,683,388.00 3,174,080.00 3,515,529 5223517 -1,922,203.50

NESTLE NIGERIA 5,342,082.00 1,602,624.60 6,944,706.60 3,699,334.00 2,526,450.00 4,199,621 5537830 2,745,085.60

NESTLE NIGERIA 3,520,211.00 1,056,063.30 4,576,274.30 2,224,667.00 1,605,183.00 2,601,394 3706923 1,974,880.30

NESTLE NIGERIA 2,435,431.00 730,629.30 3,166,060.30 1,616,840.00 1,250,550.00 1,686,266 2797545 1,479,794.30

NESTLE NIGERIA 1,758,653.00 527,595.90 2,286,248.90 877,553.00 801,829.00 1,663,107 2799919 623,141.90

NESTLE NIGERIA 2,286,009.00 685,802.70 2,971,811.70 815,768.00 710,161.00 2,333,283 3381687 638,528.70

NIGERIA ENAMELWARE

40,080.00 12,024.00 52,104.00 110,288.00 74,905.00 5,410.80 5410.8 46,693.20

NIGERIA ENAMELWARE

41,780.00 12,534.00 54,314.00 93,407.00 63,481.00 5,640.30 5640.3 48,673.70

NIGERIA ENAMELWARE

8,225.00 2,467.50 10,692.50 41,324.00 19,783.00 1,110.38 1110.375 9,582.13

NIGERIA ENAMELWARE

8,707.00 2,612.10 11,319.10 38,233.00 24,539.00 1,175.45 1175.445 10,143.66

NIGERIA ENAMELWARE

10,315.00 3,094.50 13,409.50 31,411.00 6,343.00 1,392.53 1392.525 12,016.98

NIGERIA ENAMELWARE

19,197.00 5,759.10 24,956.10 35,067.00 9,546.00 2,591.60 2591.595 22,364.51

NIGERIA ENAMELWARE

33,816.00 10,144.80 43,960.80 26,631.00 15,970.00 4,565.16 4565.16 39,395.64

NIGERIA ENAMELWARE

49,715.00 14,914.50 64,629.50 26,204.00 14,353.00 6,711.53 6711.525 57,917.98

NIGERIA ENAMELWARE

60,938.00 18,281.40 79,219.40 24,858.00 15,966.00 8,226.63 8226.63 70,992.77

178

NIGERIA ENAMELWARE

33,453.00 10,035.90 43,488.90 24,479.00 19,036.00 4,516.16 71221.155 38,972.75

NIGERIA ENAMELWARE

18,587.00 5,576.10 24,163.10 29,034.00 9,957.00 2,509.25 68647.245 21,653.86

NIGERIA ENAMELWARE

10,557.00 3,167.10 13,724.10 18,749.00 12,299.00 1,425.20 65055.195 12,298.91

NIGERIA ENAMELWARE

36,186.00 10,855.80 47,041.80 16,555.00 13,159.00 4,885.11 29622.11 42,156.69

NIGERIA ENAMELWARE

22,214.00 6,664.20 28,878.20 15,221.00 12,042.00 2,998.89 30004.89 25,879.31

NIGERIA ENAMELWARE

8,819,550.00 2,645,865.00 11,465,415.00 227,924.00 224,863.00 2,342,989 8492373 9,122,426.00

NIGERIA ENAMELWARE

5,857,605.00 1,757,281.50 7,614,886.50 347,050.00 340,768.00 3,537,638 4631561 4,077,248.50

NIGERIA ENAMELWARE

2,567,742.00 770,322.60 3,338,064.60 634,653.00 563,890.00 881,798 2571798 2,456,266.60

NIGERIA ENAMELWARE

1,345,660.00 403,698.00 1,749,358.00 146,404.00 135,830.00 1,203,696 1790362 545,662.00

NIGERIA ENAMELWARE

1,001,918.00 300,575.40 1,302,493.40 4,065.00 144,280.00 649,139 1149139 653,354.40

BETA GLASS COMPANY

8,751,300.00 2,625,390.00 11,376,690.00 1,832,403.00 1,472,444.00 1,076,409.90 1076409.9 10,300,280.10

BETA GLASS COMPANY

7,953,933.00 2,386,179.90 10,340,112.90 1,813,400.00 1,384,776.00 978,333.76 978333.76 9,361,779.14

BETA GLASS COMPANY

8,772,101.00 2,631,630.30 11,403,731.30 1,453,360.00 1,192,690.00 1,078,968.42 1078968.4 10,324,762.88

BETA GLASS COMPANY

8,835,764.00 2,650,729.20 11,486,493.20 1,056,841.00 866,252.00 1,086,798.97 1086799 10,399,694.23

BETA GLASS COMPANY

6,166,314.00 1,849,894.20 8,016,208.20 493,974.00 381,088.00 758,456.62 758456.62 7,257,751.58

BETA GLASS COMPANY

12,916,808.00 3,875,042.40 16,791,850.40 2,330,272.00 1,175,922.00 1,588,767.38 1588767.4 15,203,083.02

BETA GLASS COMPANY

14,064,994.00 4,219,498.20 18,284,492.20 687,152.00 217,115.00 1,729,994.26 1729994.3 16,554,497.94

BETA GLASS COMPANY

11,241,883.00 3,372,564.90 14,614,447.90 889,950.00 816,452.00 1,382,751.61 1382751.6 13,231,696.29

BETA GLASS COMPANY

6,059,420.00 1,817,826.00 7,877,246.00 697,709.00 636,343.00 745,308.66 745308.66 7,131,937.34

FLOUR MILL 60,631,911.00 18,189,573.30 78,821,484.30 24,937,548.00 16,947,985.00 7,457,725.05 35160908 71,363,759.25

FLOUR MILL 47,831,394.00 14,349,418.20 62,180,812.20 5,470,455.00 3,81,754 5,883,261.46 26407714 56,297,550.74

FLOUR MILL 38,603,133.00 11,580,939.90 50,184,072.90 9,878,183.00 6,363,082.00 4,748,185.36 15042834 45,435,887.54

179

FLOUR MILL 34,002,571.00 10,200,771.30 44,203,342.30 9,791,112.00 7,474,468.00 2,156,576 10922254 42,046,766.30

FLOUR MILL 26,359,103.00 7,907,730.90 34,266,833.90 1,575,948.00 4,667,612.00 1,575,948 12626503 32,690,885.90

FLOUR MILL 19,791,189.00 5,937,356.70 25,728,545.70 2,540,726.00 1,461,845.00 2,540,726 9374605 23,187,819.70

FLOUR MILL 12,754,859.00 3,826,457.70 16,581,316.70 3,348,118.00 1,370,455.00 3,348,118 7969601 13,233,198.70

FLOUR MILL 9,954,550.00 2,986,365.00 12,940,915.00 2,668,874.00 254,995.00 2,668,874 6114268 10,272,041.00

FLOUR MILL 6,973,580.00 2,092,074.00 9,065,654.00 159,696.00 1,537,104.00 159,696 2265919 8,905,958.00

FLOUR MILL 416,267.00 124,880.10 541,147.10 532,220.00 410,205.00 1,351,270 1351270 -810,122.90

FLOUR MILL 352,434.00 105,730.20 458,164.20 309,515.00 236,279.00 1,894,406 1894406 -1,436,241.80

FLOUR MILL 327,367.00 98,210.10 425,577.10 70,542.00 57,586.00 1,692,332 1692332 -1,266,754.90

FLOUR MILL 334,056.00 100,216.80 434,272.80 93,592.00 104,406.00 1,367,273 1367273 -933,000.20

FLOUR MILL 286,573.00 85,971.90 372,544.90 83,866.00 55,071.00 10,316.63 10316.628 362,228.27

FLOUR MILL 216,134.00 64,840.20 280,974.20 212,383.00 146,797.00 7,780.82 7780.824 273,193.38

FLOUR MILL 135,109.00 40,532.70 175,641.70 204,070.00 138,499.00 4,863.92 4863.924 170,777.78

FLOUR MILL 101,283.00 30,384.90 131,667.90 219,396.00 149,233.00 3,646.19 3646.188 128,021.71

NATIONAL SALT COMPANY

2,555,373.00 766,611.90 3,321,984.90 2,058,340.00 1,648,321.00 2,930,203 2930203 391,781.90

NATIONAL SALT COMPANY

2,907,900.00 872,370.00 3,780,270.00 271,448.00 1,842,346.00 2,446,300 2446300 1,333,970.00

NATIONAL SALT COMPANY

1,937,810.00 581,343.00 2,519,153.00 1,897,617.00 1,298,293.00 2,428,935 2428935 90,218.00

NATIONAL SALT COMPANY

1,416,520.00 424,956.00 1,841,476.00 1,752,331.00 1,259,873.00 2,368,851 2368851 -527,375.00

NATIONAL SALT COMPANY

59,699.00 17,909.70 77,608.70 14,930.00 14,930.00 214,842 214842 -137,233.30

NATIONAL SALT COMPANY

65,699.00 19,709.70 85,408.70 11,549.00 27,008.00 51,720 51720 33,688.70

NATIONAL SALT COMPANY

72,806.00 21,841.80 94,647.80 14,937.00 11,549.00 47,191 47191 47,456.80

NATIONAL SALT COMPANY

79,065.00 23,719.50 102,784.50 11,584.00 14,937.00 38,513 38513 64,271.50

NATIONAL SALT COMPANY

89,927.00 26,978.10 116,905.10 18,387.00 11,584.00 37,791 37791 79,114.10

P.S. MANDRIDES PLC 98,560.00 29,568.00 128,128.00 30,780.00 29,000.00 3,843.84 3843.84 124,284.16

180

P.S. MANDRIDES PLC 260,784.00 78,235.20 339,019.20 32,962.00 321,342.00 140,107 141946 198,912.20

P.S. MANDRIDES PLC 259,295.00 77,788.50 337,083.50 46,184.00 34,856.00 133,538 136131 203,545.50

P.S. MANDRIDES PLC 223,381.00 67,014.30 290,395.30 11,421.00 6,206.00 113,668 116914 176,727.30

P.S. MANDRIDES PLC 213,385.00 64,015.50 277,400.50 8,782.00 8,368.00 101,356 105274 176,044.50

P.S. MANDRIDES PLC 218,481.00 65,544.30 284,025.30 29,430.00 17,190.00 109,044 116743 174,981.30

GUINNESS 38,244,541.00 11,473,362.30 49,717,903.30 19,988,735.00 13,173,635.00 3,900,943.18 3900943.2 45,816,960.12

GUINNESS 35,897,959.00 10,769,387.70 46,667,346.70 18,991,762.00 13,541,189.00 3,661,591.82 3661591.8 43,005,754.88

GUINNESS 36,733,310.00 11,019,993.00 47,753,303.00 17,092,950.00 11,860,880.00 3,746,797.62 3746797.6 44,006,505.38

GUINNESS 30,124,847.00 9,037,454.10 39,162,301.10 14,884,450.00 10,691,060.00 3,072,734.39 3072734.4 36,089,566.71

GUINNESS 29,531,969.00 8,859,590.70 38,391,559.70 11,436,771.00 7,440,102.00 3,012,260.84 3012260.8 35,379,298.86

GUINNESS 29,129,564.00 8,738,869.20 37,868,433.20 6,276,167.00 4,859,019.00 2,971,215.53 2971215.5 34,897,217.67

GUINNESS 24,822,548.00 7,446,764.40 32,269,312.40 11,687,494.00 7,913,503.00 2,531,899.90 2531899.9 29,737,412.50

GUINNESS 16,012,252.00 4,803,675.60 20,815,927.60 9,901,668.00 6,636,335.00 1,633,249.70 1633249.7 19,182,677.90

GUINNESS 12,723,046.00 3,816,913.80 16,539,959.80 5,851,413.00 4,149,536.00 1,297,750.69 1297750.7 15,242,209.11

INTERNATIONAL BREWERIES

6,754,341.00 2,026,302.30 8,780,643.30 199,133.00 199,133.00 688,942.78 688942.78 8,091,700.52

INTERNATIONAL BREWERIES

3,069,113.00 920,733.90 3,989,846.90 285,546.00 285,546.00 313,049.53 313049.53 3,676,797.37

INTERNATIONAL BREWERIES

952,776.00 285,832.80 1,238,608.80 63,505.00 63,505.00 97,183.15 97183.152 1,141,425.65

INTERNATIONAL BREWERIES

202,516.00 60,754.80 263,270.80 118,215.00 118,215.00 20,656.63 20656.632 242,614.17

INTERNATIONAL BREWERIES

243,943.00 73,182.90 317,125.90 361,360.00 361,360.00 24,882.19 24882.186 292,243.71

INTERNATIONAL BREWERIES

256,682.00 77,004.60 333,686.60 523,657.00 523,657.00 26,181.56 26181.564 307,505.04

INTERNATIONAL BREWERIES

299,583.00 89,874.90 389,457.90 242,388.00 242,388.00 30,557.47 30557.466 358,900.43

INTERNATIONAL BREWERIES

286,769.00 86,030.70 372,799.70 142,586.00 142,586.00 29,250.44 29250.438 343,549.26

INTERNATIONAL BREWERIES

166,349.00 49,904.70 216,253.70 1,002,270.00 1,002,270.00 16,967.60 16967.598 199,286.10

NIGERIA BREWERIES 73,800,157.00 22,140,047.10 95,940,204.10 44,860,248.00 30,332 118 4,595,690 4595690 91,344,514.10

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NIGERIA BREWERIES 69,003,023.00 20,700,906.90 89,703,929.90 41,399,796.00 27,910,091.00 4,689,154 4689154 85,014,775.90

NIGERIA BREWERIES 63,557,667.00 19,067,300.10 82,624,967.10 37,579,114.00 25,700,593.00 14,150,035 14150035 68,474,932.10

NIGERIA BREWERIES 50,194,644.00 15,058,393.20 65,253,037.20 27,876,336.00 18,942,856.00 5,119,853.69 5119853.7 60,133,183.51

NIGERIA BREWERIES 49,677,817.00 14,903,345.10 64,581,162.10 16,436,255.00 10,900,524.00 5,067,137.33 5067137.3 59,514,024.77

NIGERIA BREWERIES 52,428,880.00 15,728,664.00 68,157,544.00 12,897,746.00 8,254,557.00 7,391,506 7391506 60,766,038.00

NIGERIA BREWERIES 54,448,027.00 16,334,408.10 70,782,435.10 9,148,138.00 5,086,403.00 15,195,959 15195959 55,586,476.10

NIGERIA BREWERIES 50,041,941.00 15,012,582.30 65,054,523.30 10,992,047.00 7,352,287.00 13,895,280 13895280 51,159,243.30

NIGERIA BREWERIES 37,022,763.00 11,106,828.90 48,129,591.90 10,382,429.00 7,296,446.00 4,184,639 4184639 43,944,952.90

7UP 20,528,699.00 6,158,609.70 26,687,308.70 2,635,163.00 2,277,544.00 1,970,755.10 10936755 24,716,553.60

7UP 18,592,815.00 5,577,844.50 24,170,659.50 2,223,435.00 1,529,673.00 1,784,910.24 9321693.2 22,385,749.26

7UP 14,240,754.00 4,272,226.20 18,512,980.20 2,480,798.00 1,608,910.00 1,367,112.38 7238527.4 17,145,867.82

7UP 11,240,326.00 3,372,097.80 14,612,423.80 1,960,711.00 1,219,402.00 1,079,071.30 5564817.3 13,533,352.50

7UP 12,869,000.00 3,860,700.00 16,729,700.00 1,783,900.00 1,235,424.00 1235424 15,494,276.00

7UP 7,282,982.00 2,184,894.60 9,467,876.60 1,519,526.00 954,296.00 699,166.27 7982148.3 8,768,710.33

7UP 5,025,596.00 1,507,678.80 6,533,274.80 1,686,560.00 1,143,994.00 482,457.22 5508053.2 6,050,817.58

7UP 4,019,787.00 1,205,936.10 5,225,723.10 2,008,503.00 1,382,205.00 385,899.55 4405686.6 4,839,823.55

7UP 2,148,559.00 644,567.70 2,793,126.70 1,683,604.00 1,151,394.00 206,261.66 2354820.7 2,586,865.04

7UP 1,365,766.00 409,729.80 1,775,495.80 587,960.00 397,441.00 131,113.54 1496879.5 1,644,382.26

DANGOTE FLOUR MILL

41,229,708.00 12,368,912.40 53,598,620.40 9,845,390.00 4,11,885 3,958,051.97 3958052 49,640,568.43

DANGOTE FLOUR MILL

35,238,199.00 10,571,459.70 45,809,658.70 6,583,596.00 5,374,056.00 3,382,867.10 3382867.1 42,426,791.60

DANGOTE FLOUR MILL

32,449,283.00 9,734,784.90 42,184,067.90 7,839,343.00 3,167,625.00 3,115,131.17 3115131.2 39,068,936.73

DANGOTE FLOUR MILL

27,357,655.00 8,207,296.50 35,564,951.50 4,636,107.00 675,703.00 2,626,334.88 2813346.9 32,938,616.62

DANGOTE SUGAR 15,742,539.00 4,722,761.70 20,465,300.70 16,146,930.00 11,282,240.00 1,511,283.74 1511283.7 18,954,016.96

DANGOTE SUGAR 16,969,409.00 5,090,822.70 22,060,231.70 19,586,932.00 13,185,599.00 1,629,063.26 1629063.3 20,431,168.44

DANGOTE SUGAR 14,035,716.00 4,210,714.80 18,246,430.80 39,151,378.00 21,871,042.00 1,347,428.74 1347428.7 16,899,002.06

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DANGOTE SUGAR 13,755,535.00 4,126,660.50 17,882,195.50 30,660,730.00 21,478,561.00 1,320,531.36 1320531.4 16,561,664.14

DANGOTE SUGAR 14,257,957.00 4,277,387.10 18,535,344.10 16,657,066.00 16,687,066.00 1,368,763.87 1368763.9 17,166,580.23

Source: Annual Financial Statement of Quoted Manufacturing Firms (Various Years) and Nigerian Stock Exchange Factbook (Various Years)

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