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
34
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
58
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
72
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
120
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.
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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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