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DURU, ANASTESIA NWAKAEGO
PG/PH.D/2007/46775
Impact of Working Capital Management on
Corporate Profitability of Nigerian Manufacturing
Firms: 2000 to 2011.
FACULTY OF BUSINESS ADMINISTRATION
DEPARTMENT OF ACCOUNTANCY
Ebere Omeje Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
2
Impact of Working Capital Management on
Corporate Profitability of Nigerian
Manufacturing Firms: 2000 to 2011.
BY
DURU, ANASTESIA NWAKAEGO
PG/PH.D/2007/46775
DEPARTMENT OF ACCOUNTANCY
FACULTY OF BUSINESS ADMINISTRATION
UNIVERSITY OF NIGERIA, ENUGU CAMPUS.
AUGUST, 2014
3
TITLE PAGE
Impact Of Working Capital Management On Corporate Profitability Of Nigerian Manufacturing Firms:2000 to 2011.
BY
DURU, ANASTESIA NWAKAEGO
PG/Ph.D/2007/46775
Being thesis presented to the Department of Accountancy, Faculty of Business Administration, University of Nigeria, Enugu Campus,
in partial fulfillment of the requirement of the Aw ard of Doctor of Philosophy Degree in Accountancy.
Supervisor: Prof. C.U Uche
AUGUST, 2014
Declaration
I, Duru Anastesia Nwakaego, a postgraduate student in the Department of Accountancy with Registration
Number PG/Ph.D/2007/46775, have satisfactorily completed the Requirements for research work for the
4
award of Degree of Doctor of Philosophy in Accountancy. This work incorporated in this thesis is original
and has not been submitted in part or in full for any other Diploma of this or any other University.
_________________________
Duru Anastesia Nwakaego
PG/Ph.D/2007/46775
Approval Page
This Thesis has been approved by the Department of Accountancy, Faculty of Business Administration, University of Nigeria, Enugu Campus.
5
------------------------------------ --------------------------------------
Prof. C.U. Uche Dr. (Mrs) Ofoegbu
(Project Supervisor) (Head of .Department)
Date: ________ Date: __________
Dedication
This work is dedicated to the Almighty God and to my darling husband Prince Duru Augustine
Otuosorochukwu (JP).
6
Acknowledgement
Firstly, I am heartily thankful to my supervisor Prof. C.U Uche, for his guidance, support and
encouragement which enabled me to develop deeper understanding about the subject. It is also an honour for
me to thank Dr. A. Ujunwa, who is not officially my supervisor, but he did spent much time in encouraging
me and correcting my thesis. Secondly, I owe my gratitude to the lectures in my department Dr (Mrs.) G.N.
Ofoegbu, (Head of Department) Prof (Mrs.) U. Modum, Prof. (Mrs.) R.G. Okafor, Dr. R.O. Ugwoke (
former Head of department) Dr. (Mrs.) E.O. Onyeanu, Dr.(Mrs.) A.S. Eyisi, Dr. S.E. Emengine, Mr. Osita
Aguolu, S.N. Kodjo C. Obodoekwe, L.C. Odoh, Ezuwore. C. Including Prof. J. Onwumere and Dr. E.K.
Agboeze for the corrections they gave me, among others. May God bless them in Jesus Name. I thank the
non-academic staff in the Department, Mr. Chukwuma Anikwe, Mrs. F. Enemuo, the secretary and others
for their support. I also appreciate Dr. Ekwe M.C, Dr. Ekwe K.C, Dr. Chike Nwoha, Dr. O. Chikeleze, Dr.
(Mrs) I. Okwor, Prof. I. Ndolo, Dr. A. Anyanwokoro and Dr. & Pharm. Onodugo V.(H.O.D Management)
for their encouragement. Finally, I thank my darling husband, Prince Duru A.O, my Children, Duru Austan,
Duru. Johnboblyn, Duru Confidence, and Mr. Offor Obinna, for their support and encouragement. I will not
forget to appreciate my colleagues, Ima Nnam, Zayol C, Tina C, Mrs Ude U, Chinelo O, for their
contribution, and others too numerous to mention. May the Almighty God who knows how to reward his
children reward them accordingly.
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Abstract This study examined the impact of working capital management on the profitability of Nigerian quoted
Manufacturing firms. The working capital variables studied comprise accounts payable, accounts
receivable, cash conversion cycle, stock/inventory turnover and liquidity. This study also used sales growth
and Debt as control variables in examining the impact of working capital management on the profitability of
Nigerian firms. Secondary sources of data were sourced from the Annual Reports of the 22 manufacturing
firms selected for this study for the period 2000-2011. Five Hypotheses were estimated with the use of
Generalized least square multiple regression. The findings of the study show that, accounts payable ratio
[AP] had negative relationship with the industries’ profitability. On the other hand, accounts Receivable
ratio [AR] had positive and significant relationship with profitability of the firms studied. Stock turnover
ratio had negative and significant relationship with profitability of the firms under study. Results also show
that firms cash conversion cycle [CCC] had positive but non-significant relationship with the industries
profitability, and Liquidity ratio had negative relationship with the industries profitability. Based on the
findings of the study, the following recommendations were made; there should be a balance between
liquidity and profitability. They should also avoid stock-outs because of the huge sales they made during the
years under study. They are encouraged to reduce their cost of sales to make more profit. There should also
increase their credit sales so as to have enough cash to settle their obligations. Specialized persons should be
hired by these companies for expert advice on working capital management. One of the greatest
contributions of this study is the perspective we followed in the measurement of variables (Descriptive and
four functional models of multiple regression).
8
TABLE OF CONTENTS
Title page i
Declaration ii
Approval Page iii
Dedication iv
Acknowledgement v
Abstract vi
List of Appendixes vii
List of Tables viii
Chapter One
Introduction 1
1.1 Background of the study 1
1.2 Statement of research problem 2
1.3 Objectives of the study 3
1.4 Research Questions 3
1.5 Research Hypotheses 3
1.6 Scope of the study 4
1.7 Significance of the study 4
1.8 Limitation of the study 5
1.9 Operational definition of terms 5
References 7
9
Chapter Two
Review of Related Literature 9
2.1 Introduction 9
2.2 Conceptual framework 9
2.2.1 Current Assets 10
2.2.2 Non-Current Assets 11
2.2.3 Current Liabilities 12
2.2.4 Difficulties in Managing working Capital 12
2.2.5 Overtrading 13
2.2.6 Accounts receivable management 14
2.2.7 Cash Conversion Cycle 14
2.2.8 Accounts Payable management 15
2.2.9 Liquidity management 16
2.2.10 Stocks/Inventories management 17
2.3 Theoretical framework 19
2.3.1 Operating Cycle Theory 19
2.3.2 The Importance of Operating Cycle Theory 19
2.3.3 Trade-Off Theory 20
2.4 Empirical Review 21
2.5 Summary of Literature Review 31
References 33
Chapter three
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Research Methodology 38
3.1 Research Design 38
3.2 Population and Sample size 38
3.3 Nature and Sources of Data 38
3.4 Description of Research Variables 38
3.4.1 Dependent Variable (Profitability) ` 39
3.4.2 Independent Variables 39
3.4.2.1 Accounts Receivables 39
3.4.2.2 Stock Turnover 39
3.4.2.3 Accounts Payable 39
3.4.2.4 Cash Conversion Cycle ratio 40
3.4.2.5 Liquidity ratio 40
3.5 Techniques for Analysis 41
3.6 Model Specification 42
3.7 Computed and Multiple Regression Analyse 42
References 45
Chapter Four
Data presentation and analysis 47
4.1 Introduction 47
4.1.1 Raw Data 47
4.2 Descriptive statistics 60
4.2.1 Descriptive statistics for the twenty two firms considered in the study 60
4.2.2 Food and Beverages sub –sector 61
4.2.3 Industrial and Domestic products sub- sector 62
4.2.4 Healthcare sub-sector 63
4.2.5 Building materials and chemical sub- sector 64
4.2.6 Breweries sub-sector 65
4.2.7 Packages sub-sector 66
4.2.8 Automobile and Tyre sub-sector 67
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4.2.9 A cross sub- sector comparison 68
4.2.9 Food and Beverages 68
4.2.10 Industrial and domestic Sub-Sector 69
4.2.11 Health sub-sector 70
4.2.12 Building materials and chemicals 71
4.2.13 Breweries sub-sector 72
4.2.14 Packages sub-sector 73
4.2.15 Automobile and tyre sub-sector 73
4.2.16 All the sub- sectors in Nigeria manufacturing firms 74
4.3. Correlation Matrix 76
4.3.1 Discussion of sub-sectors Result 77
4.3.1 Food and Beverages. 77
4.3.2 Industrial and Domestic Products firms 78
4.3.3 Healthcare firms 79
4.3.4. Building material, chemical and paints 80
4.3.5 Breweries firms 82
4.3.6 Packaging firms 83
4.3. 7 Automobile and tyre firms 84
4.3.8 All manufacturing firms in Nigeria. 86
4.4 Discussion of individual Industry Results 87
4.4.1. Seven – up Bottling Company 91
4.4.2. Cadbury Nigeria Plc 88
4.4.3 Flour mill Nigeria Plc 90
4.4.4 Nestle food Nigeria Plc 92
4.4.5 Nigeria Bottling Company 93
4.4.6 First Aluminium Nigeria 94
4.4.7 Aluminium Extrusion Nigeria PLC 95
4.4.8 B.O.C. Case Nigeria PLC 97
4.4.9 Enamelware Nigerian PLC 98
4.4.10 Vita Foam Plc 100
4.4.11 Vono Product Nigeria Plc 101
4.4.12 Evans Medical Nigeria 102
4.4.13 May and Baker Nigeria 103
4.4.14 Pharma-Deko Nigeria Plc 104
4.4.15 Benue cement Nigeria Plc 105
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4.4.16 Berger Paints Nigeria 106
4.4.17 Premier Paints Nigeria 107
4.4.18 Guiness Nigeria Plc 108
4.4.19 Nigeria Breweries Plc 109
4.4.20 Avon Nigeria Plc 110
4.5.2.21 Beta Glass Nigeria Plc 111
4.4.22 Incar Nigeria 112
4.4 Test of Hypotheses 113
4.5.1 Robustness test 118
4.5.2 Discussion of Findings 119
Chapter Five
5.0 Summary of findings, conclusion and Recommendations 120
5.1 Introduction 120
5.2 Summary of Research findings 120
5.2.1 Comparison of findings with Objectives of the study 120
5.3. Conclusion 124
5.4 Recommendations 124
5.5 Contribution to knowledge 125
5.6 Recommended Areas for further Research 125
Bibliography 127
Appendixes 134
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LIST OF APPENDIXES
Appendix 1 All manufacturing firms in Nigeria.
Appendix 2 The selected manufacturing firms in Nigeria.
Appendix 3 Handpicked figures of variables from annual reports and
Statement of accounts of Nigerian bottling company.
Appendix 4 Seven up Nigerian PLC.
Appendix 5 Cadbury Nigeria PLC.
Appendix 6 Flourmills Nigeria PLC.
Appendix 7 Nestle Nigeria PLC.
Appendix 8 Aluminium Extrusion industries PLC.
Appendix 9 B.O.C case PLC.
Appendix 10 Nigeria Enamelware PLC.
Appendix 11 First Aluminium Nigeria PLC.
Appendix 12 Vita Foam Nigeria PLC.
Appendix 13 Vono products PLC.
Appendix 13 Evans medical PLC
Appendix 15 May and Baker PLC.
Appendix 16 Pharma-Deko PLC.
Appendix 17 Benue Cement company PLC.
Appendix 18 Berger paints Nigeria PLC.
Appendix 19 Premier paints PLC.
Appendix 20 Guinness Nigeria PLC.
Appendix 21 Nigeria Breweries PLC
Appendix 22 Avon PLC
Appendix 23 Beta Nigerian Plc
Appendix 24 Incar Nigeria PLC
Appendix 25 Robustness Test Table
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LIST OF TABLES
4.1 Raw Data for the companies 47
4.2.1 Descriptive statistics of all the twenty two firms 60
4.2.2 Descriptive statistics of food and Beverages sub- sector 61
4.2.3 Descriptive statistics of industrial and Domestic products 62
4.2.4 Descriptive statistics of Health sub-sector 63
4.2.5 Descriptive statistics of Building materials and chemical
Sub-sector 64
4.2.6 Descriptive statistics of Breweries sub- sector 65
4.2.7 Descriptive statistics of packages sub- sector 66
4.2.8 Descriptive statistics of Auto mobile and Tyre sub-sector 67
4.2.9 A Gross section comparison of food and Beverages 68
4.2.10 A Gross section comparison of industrial and domestic products 69
4.2.11 A Gross section comparison of Health 70
4.2.12 A Gross section comparison of Building materials and chemical 71
4.2.13 A Gross section comparison of Breweries 72
4.2.14 A Gross section comparison of packages 73
4.2.15 A Gross section comparison of Automobile and Tyre 73
4.2.16 A Gross section comparison of all the sub- sector 74
4.3. Correlation matrix of pooled variables in the twenty two firms 76
4.3.1 Discussion of sub-sector Result (Regression Analysis) 77
4.3.1 Multiple Regression analysis of Food and Beverages 77
4.3.2 Multiple Regression analysis of Industrial and Domestic Products 78
4.3.3 Multiple Regression analysis of Health 79
4.3.4 Multiple Regression analysis of Building materials and chemicals 80
4.3.5 Multiple Regression analysis of Breweries 82
4.3.6 Multiple Regression analysis of Packages 83
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4.3.7 Multiple Regression analysis of Automobile and Tyre 84
4.3.8 Multiple Regression analysis of all manufacturing firms in Nigeria 86
4.4.1 Multiple Regression analysis of seven-up Bottling Company 87
4.4. 2 Multiple Regression analysis of Cad bury Nigeria Plc 88
4.4.3 Multiple Regression analysis of Flour mills Nigeria Plc 90
4.4.4 Multiple Regression analysis of Nestle Foods Nigeria Plc 92
4.4.5 Multiple Regression analysis of Nigeria Bottling Company 93
4.4.6 Multiple Regression analysis of First Aluminum Nigeria Plc 94
4.4.7 Multiple Regression analysis of Aluminum Extrusion Nigeria Plc 95
4.4.8 Multiple Regression analysis of B.O.C Case Nigeria Plc 97
4.4.9 Multiple Regression analysis of Enamelware Nigeria Plc 98
4.4.10 Multiple Regression analysis of Vita Foam Plc 100
4.4.11 Multiple Regression analysis of Vono Product Plc 101
4.4.12 Multiple Regression analysis of Evans medical Plc 102
4.4.13 Multiple Regression analysis of May and Baker 103
4.4.14 Multiple Regression analysis of Pharma-Deko Nigeria Plc 104
4.4.15 Multiple Regression analysis of Benue Cement Nigeria Plc 105
4.4.16 Multiple Regression analysis of Berger Paints Plc 106
4.4.17 Multiple Regression analysis of Premier Paints Nigeria 107
4.4.18 Multiple Regression analysis of Guinness Nigeria Plc 108
4.4. 19 Multiple Regression analysis of Nigerian Breweries Plc 109
4.4.20 Multiple Regression analysis of Avon Nigeria Plc 110
4.4.21 Multiple Regression analysis of Beta Glass Nigeria Plc 111
4.4..22 Multiple Regressio n analysis of Incar Nigeria Plc 112
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CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The sustainability of a firm heavily depends on the ability and success of its financial management function
(Karaduman et al 2011). Traditionally, corporate finance involves capital budgeting, capital structure and
working capital management, capital budgeting and structure, such as investments in fixed assets are about
the management of long-term capital and attract more attention than working capital management in finance
literature. However, working capital management is also a very important field of corporate finance, because
of its considerable effects on the firms profitability and liquidity (Nazir and Afza, 2009, Chiou, et al 2006,
and Alshubiri; 2011) In order to maintain its activity firms typically need two types of assets, fixed assets
and current assets. Fixed assets which include, building, plant, machinery, furniture, fixture and fitting
among others are not only purchased for the purpose of resale, but also for operational purposes (Singh and
Pandey, 2008). On the other hand, current assets are seen as key components of the firm`s total assets. A
firm may be able to reduce its investment on fixed assets by leasing, but this becomes practically difficult
for current assets. (Afza and Nazir 2008)
A firm’s investment in current assets such as cash, bank deposits, short term securities, accounts receivables
and inventories are called working capital. To put it differently, net working capital is the surplus of current
assets over the short term liabilities and represents the liquidity margin available to meet the cash demands
in order to maintain the daily operations and benefit from the profitable investment opportunities (Yaday,
Kamtt and Manjrekar, 2009, Padachi, 2006). Therefore it is possible to say that working capital can be
regarded as lifewire of the firm and its efficient management can ensure the success and the sustainability of
the firm while its inefficient management may lead the firm to bankruptcy (Padachi, 2006).
In this framework, working capital management represents the decision about the manipulation of ratios
which involves managing the relationship between a firm’s current assets and current liabilities. One of the
main purposes of working capital management is to provide sufficient liquidity to sustain firm’s operations
and to have to meet its obligations (Ejelly, 2004).
All firms, regardless of their size and industry need to acquire positive cash flow and liquidity (Stewart,
1995). The way that working capital is managed has also noted unworthy effects on the firm’s profitability
(Deloof, 2003). For a firm’s trading activities, working capital can be considered as a spontaneous fund, and
the amount of funds tied up to current assets can exceed that of fixed assets in many firms (Sathyamoorithi
and Wally-Drima, 2008). In this context, funds committed to working capital can be seen as hidden sources
that can be used for improving firm’s profitability (Alshubiri, 2011). Hence it is the fact that working capital
management involves a trade - off between profitability and risk. According to the theory of risk and return,
investments with higher risk may create higher returns. Thus a firm with high liquidity of working capital
17
will have low risk to meet its obligation and low profitability at the same time (Garciateruel and Martine
Solano, 2007, Zariyawati et all 2009). Therefore, efficient working capital management, plays a significant
role in overall corporate strategy in order to increase shareholder value (Dong and Su 2010) by determining
the composition and level of investments on current assets, the leve,l sources and mix of short-term debt
(Nwankwo and Osho, 2010). Especially an efficient working capital management can enable a firm to react
quickly and genuinely to unexpected changes in economic environment and gain competitive advantages
over its rivals (Alshubiri, 2011). An efficient working capital management primarily aims to ensure an
optimum balance between profitability and risk (Ricci and Viho, 2000). This objective can be achieved by
continuous monitoring of working capital components such as accounts payable, accounts receivable and
inventories. Receivables for instance are directly affected by the credit collection policy of the firm and the
frequency of converting these receivables into cash matters in the working capital management. However,
the operating cycle theory tends to be deceptive in that it suggests that current liabilities are not important in
the course of firms operation. Payables are understood to be sources of financing the firm’s activities given
this inadequacy of the operating cycle theory it is essential to infuse current liabilities in the picture to
enhance the analysis and understanding. Cash conversion theory integrates both sides of working capital that
is current assets and current liabilities. In their published seminar paper, Richard and Laughlin (1980
devised this method of working capital as part of a framework of analysis known as working capital cycle. It
claims that the method is superior to other forms of working capital analysis. In this study, Nigeria is used as
the case study because of the problems she is experiencing like other countries of the world. This area of
working capital management of firms has been neglected in spite of its importance.To the best of the
Researchers knowledge, only few Nigerians had studied on this topic. It is on this note that the researcher
has deemed it necessary to carry out a study on this area to fill the gap. Using a population sample of all the
Nigerian manufacturing companies quoted on the Nigerian stock exchange (NSE for the period 2000-
2011.The study is aimed at examining the impact of working capital management as a measure to
profitability.
1.2 Statement of research Problem
Some promising investments with high rate of return had turned out to be failures and were frustrated out of
business (Salandeen, 2001). Many factories had been either temporarily or completely shot down Example,
Nigeria paper mills ltd, jebba, Nigeria sugar company Bacita, Kastina steel rolling mill Co.Ltd, among
others. Many Nigerian workers had been thrown into unemployment market and frustratingly became
dependent on relations and friends, example, Ajaokuta steel complex reduced its staff from 5000 to 1000 in
2007. Some Nigerian manufacturing firms that are still in business cannot pay dividend to shareholders in
their companies, Example, Champion Breweries has not paid dividend since 1988, Golden Guinea
Breweries has not paid since 1997 etc. (Salandeen, 2001) Some of these companies are still shaking inspite
18
of their being quoted on the NSE. Some manufacturing firms were acquired by another because they could
not stand alone, example Savannah. Sugar Company limited was acquired by Dangote industries limited in
2002. It is in the light of this crisis that the researcher had deemed it necessary to examine the impact of
working capital management on the profitability of Nigerian manufacturing firms quoted on the NSE from
2000-2011. Working capital is the lifewire of any business enterprise. It therefore requires that the way it is
managed will to a large extent determine whether such enterprise can survive or not.The management
decides the best proportion of its investment in both fixed and current assets and finally her liability level to
enable improvement and correction of imbalances in the liquidity position of the firm. However, the
inability to make payments as at when due may definitely have serious consequences on the organizations
financial growth (profitability). Therefore, it seems important to look into the above problem to know how
to encourage managers so that their companies can stand the test of time, however, (Van Home and
Wachobvics, 2004) pointed out that excessive level of current assets may have a negative effect on a firm’s
profitability whereas a low level of current assets may lead to lowers of liquidity and stock-out, resulting in
difficulties in maintaining smooth operations.
1.3 Objectives of the Study
The general objective of this study is to examine the impact of working capital management on the
profitability of Nigerian manufacturing firms. Thus the objectives of this study shall specifically be:
1. To determine the impact of accounts payable ratio on corporate profitability.
2. To ascertain the impact of accounts receivable ratio on corporate profitability.
3. To ascertain the impact of cash conversion cycle (CCC) ratio on profitability.
4. To investigate the relationship between stock turnover ratio and firm profitability.
5. To determine the impact of liquidity ratios on the profitability of Nigeria quoted Manufacturing
firms.
1.4 Research Questions
The following research questions will be considered in the study.
1. To what extent does accounts payable ratio influence profitability?
2. To what extent does accounts receivable ratio influence profitability?
3. How far has cash conversion circle ratio affected the profitability of the companies under study?
4. To what extent does stock turnover ratio influence firm profitability?
5. To what extent does liquidity ratio influence the profitability of Nigeria quoted manufacturing firms
under study?
1.5 Research Hypotheses
In order to address the issue raised above, the following hypotheses shall be proved:
19
1. Accounts payable ratio has no significant and positive impact on corporate profitability.
2. Accounts receivable ratio has no significant and positive impact on corporate profitability.
3. There is no significant and positive impact of cash conversion cycle ratio on profitability of the
Nigeria quoted manufacturing firms.
4. There is no relationship between stock turnover ratio and firm profitability.
5. There is no relationship between liquidity ratio and profitability of the Nigeria quoted manufacturing
firms.
1.6 Scope of the Study
The study is on the impact of working capital management as a measure for profitability following previous
studies on this area, the study focuses on five independent variables, Accounts receivable, Accounts payable,
inventory , cash conversion cycle and liquidity. The study also focuses on Dependent
variable[Profitability],five independent variables[Accounts Payable,Accounts Receivable,Cash Conversion
Cycle, Stocks,Liquidity, and other control variables that affect profitability such as sales growth and debt.
The study is for the period: 2000-2011, and it will include all the publicly listed manufacturing firms in
Nigeria.
1.7 Significance of the study.
It was mentioned earlier in this study that working capital is the life wire of organization. It is assumed that
what blood is to human existence is what working capital is to business. Therefore a well designed and
implemented working capital management is expected to contribute positively to a firm value (Padachi,
2006). It is expected that this study will:
a. Help to create awareness on the impact of working capital management and how it can enhance
corporate profitability.
b. Help managers of the firms under study to have better insights on how to maximize their firms value.
c. Help investors to invest in the manufacturing companies under study that are managing their working
capital well. These investors will have more confidence in the company they want to invest in. Their
investing in Nigeria will influence the growth of the economy.
d. It will also assist policy makers to implement new set of policies regarding working capital
management in Nigeria to ensure continuous economic growth.
e. Meet the need of management accountants, academia, and students who will be interested in this
study. Other researchers on corporate governance will find useful information from this study, it will
also add to the existing literature on the topic.
20
1.8 Limitation of the study
The study was conducted on only manufacturing firms in Nigeria Accordingly the result could not be
generalized for all the manufacturing firms operating in Nigeria due to unavailability of data for some of
these firms.
1.9 Operational Definition of Terms
Working capital: working capital is the cash needed to pay for the day-to-day operation of the business.
It is calculated as the difference between the current assets of a business and its current liabilities.
Current assets are those assets that are held in cash form or that can easily be turned into cash. Examples
are: receivable, inventory and cash. While current liabilities are money owned by a business which will
need to be paid within one year.
Working capital management: it is the regular adjustment and control of the balance of current assets and
current liabilities of an organization are made and the fixed assets are properly serviced. (Ross et al
1996) Accounts receivable are customers who have not yet made payment for goods or services, which
the firm has provided. The objective of the debtor management is to minimize the time-lapsed between
completion of sales and receipts of payment. In this respect account receivable is divided by sales. It
represents the firms’ payment from its customers.
Inventories: Inventories are list of stocks raw materials, work-in- progress or finished goods waiting to
be consumed in production or to be sold. Inventory is calculated as inventory/purchase. It reflects the
stock held by the firm.
Accounts payable: accounts payable is suppliers whose invoices for goods or services have been
processed but who have not yet been paid. Organization often regards the amount owing to the creditors
as a source of free credit. Account payable is calculated as payables divided by purchases. The longer
the value, the longer firms take to settle their payment commitment to their suppliers.
Cash conversion cycle (CCC): the cash conversion cycle (CCC) is a proxy for working capital
management efficiency. Cash conversion cycle is the flow of cash from suppliers to inventories to
accounts receivable and back into cash. It is therefore calculated as inventories and receivables less
inventories and payables. It has been interpreted as a time interval between the cash outlays that arise
during the production of output and the cash inflows that results from the sale of the output and
collection of the account receivable. CCC is calculated by subtracting the payables the sum of the
inventory conversion period and the receivable.
Sales growth: the sales growth is the increase or decrease of the annual sales measured as a percentage.
In this study a positive effect from sales growth on the performance is assured.
Debt: This is measured by relationship of long-term debt to total assets and is proxy leverage. It is
assumed that when external funds are borrowed e.g. from banks at the fixed rate, they can be interested
in the company and gain a higher interest paid to the bank.
21
Working capital cycle:- the period of time between the point at which the cash is first spent on the
production of a product and the final collection of cash from a customer.
Overtrading:- Overtrading is the term applied to a company which increase its turnover without having
sufficient capital backing. It is risky because short-term finance can be withdrawn relatively quickly if
creditors lose confidence in the business or if there is a general tighten in the economy. The problem
with overtrading is not that the company is unprofitable; it is that company has simply run out of cash.
22
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Retail Domestic company in Botswana. Journal of management Research 17, 7-24.
Singh, J.P and Pandey, S. (2008). Impact of working capital management in the profitability of Hindalo
industries limited. lcfai University Journal of financial Economic 6 (4) 62 – 72
23
Yaday, R. Kamath, V. and Manjrekar, P. (2009). Working capital management: A study of Maharashtra’s
Bulk Drugs listed companies. Chemical Business 23,(7) 27 – 34.
24
CHAPTER TWO
REVIEW OF RELATED LITERATURE
2.1 INTRODUCTION
This Part of the work discusses the conceptual frame work, theoretical frame work and
empirical review of working capital management on corporate profitability of firms.
The study of working capital management and profitability is becoming relevant because many
organizations in the recent past had fallen a victim of premature liquidation as a result of inadequate
attention to management of working capital from the management of the affected firms. The working
capital meets the short term financial requirement of a business enterprise. It is trading capital, not retained
in the business in a particular form longer than a year. The money invested in it changes form and
substance during the normal course of business operations. The need for maintaining an adequate working
capital can hardly be questioned. Just as circulation of blood is very necessary in the human body to
maintain life the flow of funds is very necessary to maintain business. If it becomes weak, the business can
hardly prosper and survive (Padachi, 2006).
2.2 Conceptual Framework
The successes of a firm depend ultimately, on its ability to generate cash receipt in excess of disbursement.
The cash flow problems of many businesses are exacerbated by poor financial management and in
particular the lack of planning cash requirement (Jarvis et al, 1996). The ultimate objective of a firm is to
maximize the profit, but preserving liquidity of the firm is also an important objective. The problem is that
increasing profit at the cost of liquidity can bring serious problems to the firm. Therefore, there must be
trade-off between these two objectives of the firm. One objective should not be at the cost of the other,
because both have their importance. If firms do not care about profit, they cannot survive for a longer
period. On the other hand, if firms do not care about liquidity, they may face the problem of insolvency or
bankruptcy, for these reasons working capital management should be given proper consideration and will
ultimately affect the profitability of the firm (Ricci and Vito, 2011)
Lamberson (1995) showed that working capital management has become one of the most important issues
in organizations where many financial managers are finding it difficult to identify the important drivers of
working capital. As a result companies can minimize risk and improve their overall performance if they can
understand the role and determinant of working capital. Olajide (2011) stated the components of working
capital as:
� Stocks (Inventory)
� Debtors (Recordable)
25
� Creditors (Payable)
� Cash
� He went on to say that the management of these various components of working capital involves the
following:
� What level do we maintain for each component?
� How do we finance the optimal level defined?
� What ratio do we maintain current assets and current liabilities. The following factors will inform
management in making the above decisions.
� The nature of the product on service of the company.
� The practice in the industry in which the company operates.
� The sales pattern of the company’s product e.g suppliers, bankers and so on.
� The short – term investment opportunities available
� The financial management style of the company.
Osisioma, (1996), opines that the difference between current assets and current liabilities is referred to as
working capital which forms the liquid buffer available in meeting future financial demands and
contingencies of the organization.
2.2.1 CURRENT ASSETS
The term current assets is used to designate cash and other asset or resources commonly identified as
those which are reasonably expected to be realized in cash or sold or consumed during the normal
operating cycle of a business. Thus the term comprehends in general such resources as:
� Cash available for current operations and items which are the equivalent of cash.
� Inventories (or stocks) of merchandise, raw material goods in process, finished goods, operating
supplies, and ordinary maintenance material and parts.
� Trade accounts notes and acceptance receivable.
� Receivable from officers, employees, affiliates, and others, if collectible in the ordinary course of
business within a year.
� Installment or deferred accounts and notes receivable if they conform generally to normal trade
practices and terms within the business.
� Marketable securities representing the investment of cash available for current operations and
� Prepaid expenses such as insurance, interest rents taxes, unused royalties, current paid adverting
service not yet receivable and operating supplies.
These forms of current assets are generally grouped into
1. Cash
2. Cash equivalent (that is, temporary investment)
26
3. Accounts and note receivable
4. Inventories (stocks)
5. Prepaid expenses.
Cash is of course, the ultimate measure of a current asset since current liabilities are paid off in cash.
Compensation balance under bank loan agreements cannot in most cases, be regarded as free cash
(Osisioma,1996). Cash equivalent represents temporary investment of cash in excess of current requirement
made for the purpose of earning must be alert to the valuation of such investments. The mere ability to
convert an asset to cash is not the sole determination of its current nature. It is the intention and normal
practice that governs. Intention is however, not always enough. Thus, the cost sale should be included in
current assets commitment from a buyer to purchase the asset at a given price within the following operating
cycle. Accounts receivable (that is debtors) net of provision for uncollectible accounts, are current unless
they represent receivable for sales, not in the ordinary course of business, which are due after one year.
Installment receivables from customary sales usually fall within the operating cycle of the company.
Financial managers must be alert to the valuation as well as validity of receivable particularly in case such as
those where sale are made on consignment or subject to the right of return. Receivables from affiliated
companies or from officers and employees can be considered current only if they are collectible in the
ordinary course of business within a year or in the case of installment sales, within the operating circle.
Inventories (or stocks) are considered current assets except in case where they are in from inventories, such
as tobacco, which require a long aging cycle (Brealey and Steward,1981). Prepaid expenses are considered
current, not because they can be converted into cash but rather because they represent advance payments and
service and supplies which would otherwise require the current outlay of cash
2.2.2. Non-Current Assets.
The items listed below are generally considered as non-current.
� Cash and cash claims restricted to used for other than current operations, designated for the
acquisition of non – current assets, or segregated for the liquidation of non-current debts.
� Advance and investment insecurities, whether marketable or not, made for purpose of control,
affiliation or other continuing business advantage.
� Cash surrender value of life insurance polices
� Land and other natural resources
� Depreciable assets
� Long – term prepayment fairly chargeable to the operation of several years.
27
2.2.3 Current Liabilities
The term current liabilities is used principally to designate obligations whose liquidation is
reasonable expected to require the use of existing resource properly classifiable as current assets, or
the creation of other current liabilities (Larson,1990). As a balance sheet category, the classification
is intended to include obligation for items which have entered into the operating cycle. Such as
payable incurred in the acquisition of material and supplies to be used in the production of goods or
in providing services to be offered for sale; collections received in advance of delivery of goods or
performance of service, or debts which arise from operations directly related to the operating cycle,
such as accruals for wages, salaries, commission, rentals, royalties, and income and other taxes.
Other liabilities whose regular and ordinary liquidation is expected to occur within a relatively short
period of time, usually twelve months, are also intended for inclusion such as short – term debts
arising from the acquisition of capital assets, serial maturities of long – term obligation amounts
required to be expended within one year under sinking find provision, and agency obligation arising
from the collection or acceptance of cash or other assets for the account of third persons. Current
liabilities are, therefore, obligations which would generally require the use of current assets for their
discharge or alternatively, the creation of other current liabilities. The following are current liabilities
commonly found in practice.
� Account payable (or trade creditor)
� Notes payable
� Short – term bank and other loans
� Tax and other expenses accruals
� Current portion of long-term debt.
The current liabilities classification does not generally include the following items, since they do not require
the use of resource classified current:
� Short – term obligations expected to be refinanced.
� Debts to be liquidated from hands that have been accumulated and are reported as non-current assets
� Loads on life insurance policies made with the intent, these will not be paid but will be the policies
upon their maturity on cancellation.
� Obligation for advance collections that involve long – term deferment of the delivery of goods or
services.
2.2.4 Difficulties in Managing Working Capital
A financial manager spends a lot of time in handing current assets of a firm. This is so because the
level of each component of current assets changes continually. For instance, accounts receivable and
inventory increase and decrease with the level of sales while payable expand and contrast with the
28
level of purchases. Equally, the level of cash reduces as management uses cash to pay taxes and
other bills. Therefore, managers must be up and doing in monitoring each of these changes so as to
avoid financial difficulties that could put the company into financial mess and embarrassment.
Cooley and Rodin (1988) observed that changes in both current assets and current liabilities relate
closely to changes in a firm’s selling activity. These changes include changes in inventory, accounts
receivable, account payable, cash overdraft, taxes and other bills payable. All these change emanate
because in a firms most liquid of all assets, which is cash by analyzing a statement of cash flow. As
already stated, a firm uses its liquid asset especially cash to pay its suppliers, employee and creditors,
working capital that is synonym for current assets effect a firm’s ability to pay. Short-term maturity
obligations. The financial manager in his effort to match the maturities of capital sources with the
maturities of their used provides some assurance that a firm will be able to pay its obligations. All
these analysis provide a financial managers with tedious talks that are time consuming and energy
sapping. In other words, the management of current asset is problematic. (Pandey, 2000). Profit
maximization is the ultimate objective of firm as well as protecting liquidity is an important
objective too. The difficulty of working capital management is to achieve the two objectives
optimally within an operating period if profit increases at the cost of liquidity, this may create serious
problem to firms. Therefore, to solve such problem, there must be some compromise between these
two objective of firms. One objective will not achieve at the cost of the other, as both objectives have
their own importance to firms. If firm do not care about profitability, they may not survive for a
longer period. On the other hand, if firms do not care about liquidity, they may face problem of
insolvency or bankruptcy.
2.2.5 Overtrading
When a company is trading large volume of sales very quickly, it may also be generating large
amounts of credit sales, and as a result large volume of trade receivables, it will also be purchasing
large amounts of inventories on credit to maintain production at the same rate as sales and therefore
have large volumes of trade payables. This will extend the working capital cycle which will have an
adverse effect on cash flow if the company doesn’t have enough working capital, it will find it
difficult to continue as there would be insufficient fund to meet all costs as they fall due (Faris et
all,2002). Overtrading occurs when a company has inadequate finance for working capital to support
its level of trading. The company is growing rapidly and is trying to take on more business that its
financial resources permit i.e it is undercapitalized; overtrading typically occurs in businesses which
have first started to trade and where they may have suddenly begun to experience rapid sales growth.
In this situation it is quite easy to place high importance on sales growth while neglecting to manage
29
the working capital. Overtrading may result in insolvency which means a company has severe cash
flow problems, and that a thriving company, which many look very
profitable, is failing to meet its liabilities due to cash shortages.
2.2.6. Accounts Receivable management.
Profit may only be called real profit after the receivables are turned into cash. The management of
accounts receivable is largely influenced by the credit policy and collection procedure. A credit policy
specifies requirements to value the worth of customers and a collection procedure provides guidelines
to collect unpaid invoice that will reduce delays for customers who have not yet made payment for
goods or services and outstanding receivables (Hills and Sartoris, 1992, Richards & Laughlin, 1980).
Aligning the management between cash inventory and payable are important, and a stimulus to
researchers studies to integrate the working capital management (wcm) components. Accounts
receivables which the firm has provided; the objective of debtor management is to minimize the time
lapse between completions of sales and receipts of payment. In this respect accounts receivable (AR)
is calculated as receivables divided by sales. This variable represents the rate at which the firm
collects payment from its customers. (Falope and Ayilore (2009), Basley and
Brigham(2005).SamiLoglu and Demirqunes (2008), Sharma and kumar (2011). The above authors
examined the influence accounts receivable has on profitability in their different countries.
2.2.7 Cash conversion cycle management.
Cash conversion circle definition is not constant for example; Stewart (1995) defined cash conversion
cycle as a composite metric describing the average naira investment in material into a dollar collected
from a customer: Besley and Brigham (2005) described cash conversion cycle as the length of time
from the payment for the purchase of raw materials to manufacture a product until the collection of
account receivable associated with high profitability because it improves the efficiency of using the
working capital. Although the length of cash conversion cycle is an important measure of the
efficiency of working capital management, the cash conversion cycle introduced by Richards and
Laughlin (1980) is a powerful performance measure for assisting how well a company is managing its
working capital. Vaidy et al. (1990) argued that a short cash conversion cycle is indirectly related to
firm value. Short cash conversion cycle indicate that the firm is collecting the receivable as quickly as
possible and delaying the payments of suppliers as slowly as possible. This leads to high net present
value of cash flow and high firm value. Cash conversion definitions are not constant, for example
steward (1995) defines cash conversion cycle as a composition metric describing the average days
required to turn naira invested in raw material into a naira collected from a customer. Besley and
Brigham (2005) described cash conversion cycle as the length of time from the payment for the
purchase of raw materials to manufacture a product until the collection of account receivable
associated with the sale of the product. Shorter cash conversion cycle could be associated with high
30
profitability because it improves the efficiency of using the working capital, although the length of
cash conversion cycle is as important measure of the efficiency of working capital management, little
is known about the effect of cash conversion cycle on firms profit ability. The main reason for this
lack of knowledge is that there are few cash conversion cycle studies and that managers of companies
are not aware of their importance. Among the few studies that tested the effect of cash conversion
cycle on profitability is the study of shin and semen (1998). In their study they used a large of listed
American firms covering the period 1975-1994. Their results showed a strong negative relation
between the length of the cash conversion cycle and corporate profitability. Karaduman et al (2011) in
their study found out that reducing cash conversion circle positively affects return on assets. Kwasi
(2010) also opined that there are inconsistent trends in the various components of working capital. He
also found a significant negative relation between profitability and number of day’s accounts
receivable, trade cycle. Deloof (2003), in his study found out that there was a negative relationship
between profitability that measured by gross operating income and cash conversion cycle as well as
number of days accounts receivable and inventories. He suggested that managers can increase
corporate profitability by reducing CCC, the managers can increase corporate profitability by
reducing the number of days accounts receivable and inventories. Mccarty, and Lyroudi (1993) found
out that cash conversion cycle negatively related with current ratio but positively related with quick
ratio. In addition the study revealed difference between the concept of cash conversion cycle in
manufacturer, retail, wholesale and service industries.
Gill et al (2010) sought to extend Tryforidis findings regarding the relationship between working
capital management and profitability. They found out statistically significant relationship between the
cash conversion cycle profitability measured through gross operating profit.
2.2.8 Accounts payable management.
Accounts payable is one of the major sources of secured short- term financing (Gitman 2009, till and
sarton 1992). Utilizing the value of relationship with payee is a sound objective that should be
highlighted as important as having the optimal level of preventions (Hill and sartorial 1992). As a
consequence strong alliance between company and its suppliers will strategically improve production
lines and strengthen credit record for future expansion. Singh, (2004) stated that the liquidity of
Positionary firm mainly depends, upon accounts receivable collection and payable deferred policy as
well as inventories conversion period of firm.
Creditor is a vital part of effective cash position purchasing initiates cash outflows and over – zealous
purchasing function can create liquidity problems. Consider the following:
� Who authorizes purchasing in your company – is it tightly managed or spread many a number
of (junior) people?
31
� Are purchase quantities geared to demand forecasts.
� Do you use order quantities which take account of stock - holding and purchasing cost?
� Do you know the cost to the company of carrying stock?
� Do you have alternative source of supply? If not, get quotes from major suppliers and shop
around for the best discounts, credit terms, and reduce dependence on a single supplier.
� Hoe many of you supplier have a return policy?
� Are you in a position to pass on cost increase quickly through price increases to you
customers?
� If a supplier of good or service let you down can you charge back the cost of the delay?
� Can you arrange (with confidence) to have delivery of supplier.
Staggered or on a just in time basis?
There is an adage in business that if you can buy well then you call sell well. Management of your creditors
and supplier is just important as the management of your debtors.
2.2.9 Liquidity management.
Liquidity management is necessary for all businesses, small or large because, it means collecting cash from
customers so that having no difficulty in paying short term debts will be achieved.
Therefore, when a business does not mange its liquidity well, it will have cash shortages and will result in
difficulty in paying obligations. As a result, in addition to profitability, liquidity management is vital for
ongoing concern, corporate liquidity is examined from two dimensions: static or dynamic view (Lancaster et
al, 1999, fair and Hutchison 2002, and moss and Stine, 1993). The static view is based on commonly used
traditional rations, such as current ratio and quick ratio, calculated from the balance sheet amounts these
ratio measure liquidity at a given point in time whereas dynamic view measure on going liquidity from the
firms operations. As a dynamic measure of the time it takes a firm. To go from cash outflow to cash inflow
which is measured by cash conversion cycle? The study that empirically examined the relationship between
profitability and liquidity showed that there exists a significant and negative relation between profitability
and CCC (Jose et al, 1996, Eljelly, 2004) another study conducted over 22,000 public companies by
Hutchison et al 2007). Indicated a direct correlation between shorter CCC and higher profitability for 75%
of industries. Schilling (1996) mention optimum liquidity position, which is minimum level of liquidity
necessary to support a given level of business activity in his writing. Briefly, he says it is critical to deploy
resources between working. Capital and capital investment, because this return on investment is usually less
than the return on capital investment. Therefore, deploying resources on working capital as much as to
maintain optimum liquidity position is necessary. Then he sets up the relationship between CCC and
minimum liquidity required such that if CCC lengthens, the minimum liquidity required decreases. The two
key ratios that can calculated to provide a position of a business are:
32
⇒ Current ratio
⇒ Acid test (quick) ratio
Current ratio = Current Assets Current liabilities Quick ratio = Current asset Inventories Current liabilities Referring to the theory of risk and return, investment with more risk will result to more return. Thus, firms
with high liquidity of working capital may have low risk then low profitability. Conversely, firm that has
low liquidity of working capital, facing high risk results to high profitability. The issue is in managing
liquidity, firm must take into consideration all the items in both accounts and try to balance the risk and
return. However, van home and wachowicz (2004) pointed out that excessive level of current assets may
have a ergative effect of a firm’s profitability whereas a how level of current assets may lead to lowers of
liquidity and stock – outs, resulting in difficulties in maintaining smooth operations. The ratios used are
chosen from those utilized by Bhunia (2007), and Singh et al (2008). Enyi (2005), in his study revealed that
firms with adequate working capital related to their operational size have performed better that firms which
have less working capital in relation with their operational size.
Singh, (2004) stated that the liquidity position of any firm mainly depends upon account receivable
collection and payable deferred policy as well as inventories conversion period of firms,
Ejelly, (2004) elucidated that efficient liquidity management involves Planning and controlling current
assets and current liabilities in such a manner that elimunates that the inability to meet one short term
obligation and avoids excision investment in these assets. He examined the relationship between profitability
and liquidity as measure by current ratio and cash gap (cash conversion cycle). He found out that the cash
conversion cycle was more important as a measure of liquidity than the current ratio that affects
profitability. The results were stable, and had important implication for liquidity management Olugbenga
(2010) in his comparative study in Nigeria found out most Nigeria companies suffer from inadequacy of
liquid assets to meet their short term financial obligations. He recommended that companies should strive to
maintain optional level; short term bank facilities should be a last resort.
2.2.10 Stock/ Inventory Management
Stock constitute a substantial proportion of the current asset group. It represents investments made for the
purpose of obtaining a return. The return is derived from the expected profits which may result from sale. In
most companies a certain level of inventory must be kept order to generate an adequate level of sales. If the
stock level is inadequate, the sales volume will fall below the level otherwise attainable. Excessive stocks,
on the other hand, expose the company to expenses such as storage costs, insurance and taxes, as well as risk
of loss of value through obsolescence and physical deterioration moreover; excessive stocks tie up funds
which can be used more profitably elsewhere. Owing to the risk involved in holding inventories/stocks as
33
well as the fact that stocks are one step further removed from cash than receivables (because they have to be
sold before they are converted into receivables) stocks are normally considered the least liquid component of
current assets group (Osisioma, 1998).
Inventory/turnover ratio
Inventory/stock turnover equals cost of goods sold divided by average inventories/stock. This ratio measure
the average rate of speed with which inventories move through and out of the company. The equation is:
inventory/stock turnover ratio Cost of Goods Sold
Average Inventory/Stock for the Year.
A low inventory/stock turnover ratio implies a large investment in inventories relative to the amount needed
to service sales. Excess stock ties up resource unproductively. On the other had if the stock turnover ratio is
too low, stocks are too small and it may be that the company is constantly running short of inventory (out of
stock), thereby losing customers. The objective is to maintain a level of inventory relative to sale that is not
excessive but at the same time is stuffiest to meet customer needs.
Note that the average inventory figure is most readily obtained as:
Average inventory/stock + closing inventory/stock 2
Inventory/stock turnover is calculated as:
Inventories/stocks Purchases Stocks represent investments made for the purpose of obtaining a return. The return is derived from the
expected profit which may result from sales.
Falope and Ajilore (2009) found out in their study in Nigeria a significant negative relationship
between net operating profitability and the average collecting period inventory turnover in days, average
payment period and cash conversion cycle.
Inventory play an important role to determine the activities in producing, marketing and purchasing since
inventory determined the level of activities in a company managing, it strategically contributes to
profitability (Hill & Sactoris, 1992) suppler selection process and inventory management are reciprocal to
enables companies to deal with uncertainties of container demand. Further more, a company’s ability to
respond to demand is largely dependent on how efficient the company manages inventories and how
committed its suppliers are to support a company’s production lines.
34
2.3 THEORETICAL FRAMEWORK
2.3.1 Operating Cycle theory
To estimate the gross working capital requirements, the understanding of the operating cycle is very
important. The function of any trading unit is to procure material, process the same, sell the finished goods
and realize money and utilize the money so received, to procure material again and to continue the cycle all
over again. Thus the process starts with purchase of materials required for the trading. The process purchase
of material may take some time due to the number and nature of material transportation, the material once
procured are made to undergo the several processes, the duration of which may range from a day to months.
During this period, various material will be in different stages of production in different forms. Besides, the
cost of material, labour charges, electricity, water, rent etc are also incurred during the period of processing.
All these required funds/capital once the goods are produced it may not be sold immediately and it may have
to be stored in a go down for some days before they are sold. Storing of such finished goods involves cost of
materials used in such finished products, labour and other manufacturing expresses incurred in producing
them. It is not necessary that all the goods will be in cash.
Some goods will be sold on credit till such time sale proceeds are not realized, find are blocked in such
receivable. Finally when the sales proceeds are realized the funds are again used to procure materials as
above and the whole process cycle starts all over again. The total time taken from the purchase of materials,
till realization of sale proceeds is called the operating cycle and amount of capital required to sustain this
cycle is called gross working capital (Ghosh et all 2004)
2.3.2 The Importance of Operation Cycle Theory
Operating Cycle is important because it determines the amount of working capital a business needs.
If you can have the operating cycle, you will have the working capital requirement of the business. If the
turnover period for inventories and account receivable lengthen, or the payment period to account payable
shortens, then the operating cycle will lengthen and the investment in working capital will increase (Ghosh
et al 2004).
35
OPERATING CYCLE DIAGRAM
Source: Ghosh, et al (2004)
2.3.3 TRADE - OFF THEORY
The trade – off theory refers to the idea that a company chooses how much debt finance and how much
equality finance to use by balancing the cost and benefits. The classical version of the hypothesis goes back
to Kraus and Lichtenberger (1973) who considered a balance between the deed–weight. Cost of bankruptcy
and the serving benefit of debt. Often agency costs are also included in the balance.
This theory is often set up as a competitor theory to the pecking order theory of capital structure. An
important purposed of the theory is to explain the fact that corporation usually are financed partly with debt
and partly with equity. It states that there is an advantage to financing with debt the tax benefits of debt and
there is a cost of financing with debt the costs of financing distress including bankruptcy cost e.g staff
leaving, suppliers demanding disadvantage payment terms, bondholder/stockholder infighting, etc the
marginal benefits of further increase in debt declines as debt increases while the marginal cost increases, so
that the 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 empirical relevance of the trade-off theory has often been questioned by miller (1977) for example
compared this balance between horse and rabbit content in a stew of one horse and they are sure, while
bankruptcy is rare and, according to miller, it has low deed – weight cost. Accordingly he suggested that if
Cash Advance
Raw Materials
W.I.P
Finished goods
Debtor
36
trade off theory were true, firm ought to have much higher debt level than we observe in reality. Meyer
(1994) was a particularly fierce critic in his presidential address to the American Finance Association
meeting in which he proposed what he called the pecking order theory. Fama and French (1992) criticized
both the trade – off theory and the pecking order theory in different ways. Welch (2012) has argued that
firms do not undo the impact of stock price as they showed under the basic trade – off.
2.4 EMPIRICAL REVIEW
Many previous researchers have indicated working capital management and corporate profitability of
firms in different countries and environments.
Samiloglu and Demirqunes (2008) analyze the effect of working capital management on firm
profitability. In accordance with the aim, they considered between firm profitability and the
components of statistically significant relationship between firm profitability and the components of
cash conversion cycle at length, a sample consisting of Istanbul stock exchange (ISE) listed
manufacturing firm for the period of 1998 – 2007 has been analyzed under a multiple regression
model. Empirical finding of the study showed that accounts receivable period inventory period and
leverage affect firm profitability negatively while growth (in sales) affects profitability positively.
Sharma and Kumar (2011) examine the effect of working capital on profitability of India firm. They
collected data about a sample of 263 non financial BSE 500 firms listed at the Bombay Stock
Exchange (BSE) from 2000 to 2008 and evaluated the data using ordinary least square (OLS)
multiple regression. The finding of their study significantly depart from the various international,
studies conducted in different markets. The result revealed that working capital management and
profitability was positively correlated in Indian companies. The study further revealed that
inventory number of days and number of days accounts receivable and cash conversion – period
exhibit a positive relationship with corporate profitability.
Adina (2010) states in his paper working capital management and profitability: A case of Alba
county companies that the purpose of his study was to analyze the efficiency of working capital
management from Alba County. He examined the relationship between the efficiency of the
working capital management and profitability using person correlation analyses and using a sample
of 20 annual financial statement of companies covering period 2004 – 2008. He concluded that
there was a weak negative linear correlation between working capital management indicator and
profitability rates
Karaduman, et al (2011), examines the empirical relationship between efficiency of working capital
management and corporate profitability of selected companied in the Istanbul stock exchange for
the period of 2005 – 2009. The panel data methods were employed in order to analyze the
mentioned relationship. The cash conversion cycle (CCC) was used as a measure of working capital
37
management efficiency, and return on assets (ROA) used as a measure of profitability. He found out
that reducing cash conversion circle (CCC) positively affects return on assets.
Charitou, et al (2010) in their study empirically investigate the effect of working capital
management on firm’s financial performance in an emerging market. They hypothesized that
working capital management leads to improved profitability. Their data set consists of firms listed
in the Cyprus Stock exchange for the Period 1998-2007. Using multivariate regression analysis,
their results supported their hypotheses. Specifically, their results indicated that the cash conversion
cycle and all its major components namely, days in inventory, days in sales outstanding and
creditor’s payment-period were associated with the firms’ profitability. They opined that the results
of this study should be of great importance to managers and major stakeholders, such as investors,
creditors and financial analysts, especially after the recent global financial crisis and the latest
collapse of giant organizations worldwide.
Kwasi (2010) in his attempt to measure and analyze the trends in working capital management of
Ghanaian Oil market firm and its impact on their performance. This was very crucial because of the
purported high profitable level of the sector and likely under-utilization of such profit potential. The
study employed trend and econometric analysis using an unbalanced planed data of 11 Ghanaian oil
marketing firms from 2001-2008. for the econometric analyses, the study adopted the number of
days inventory, number of days accounts Receivable, number of days payable, cash conversion cycle
and the ret trade cycle as measure of working capital management, and gross profit divided by total
assets as profitability. He found out inconsistent trend in the various components of working in the
Ghanaian oil marketing companies (OMCs). He also fond a significant negative relation between
profitability and number of days accounts receivables number of days payables, the cash conversion
cycle and the net trade cycle.
Bhunia A and Khan I.V (2011) in their study liquidity management efficiency of Indian steel
companies (a case study) stated that liquidity management is of crucial importance in financial
management division. They want on to say that the optimal of liquidity management could be
achieved by company that manages the trade – off between profitability and liquidity management.
The paper analyzed the association between the liquidity management and profitability of 230 India
private sectors, steel companies obtained from CMIE database. Liquidity management indicators and
profitability indicator over the period from 2002 to 2010 were modeled as a linear regression system
in multiple correlation and regression analysis. Evidence of petite association between those variable
was found. A descriptive statistic disclosed that liquidity and solvency position was very satisfactory
and relatively efficient liquidity management was found. Multiple regression test confirmed a lower
degree of association between the liquidity management and profitability.
38
Chring, Novazzi and Gerah (2011) examine the relationship between working capital management
and profitability in Brazilian listed companies. Their objective were of two folds, to investigate if
there was any difference between corporate groups of companies: working capital intensive and
fixed capital intensive, and to identify the variables that mostly affect profitability. The profitability
was measured in three different ways: return on sales (ROS), on asset (ROA) and on equity (ROE).
The independent variables were cash conversion efficiency, debt ratio, days of working capital days
receivable and days inventory. Two samples were obtained consisting of 16 Brazilian listed
companies in each group for the period 2005 – 2009. Multiple linear regressions have identified that,
as far as ROS and ROA are concerned, to mange working capital properly is equally relevant for the
two groups of companies. Relevant in the company profitability in the fixed capital group as opposed
to the working capital group. From ANOVA it was evident that days inventory has negative
relationship with ROS and ROA but has no statistical evidence in ROE improvement in working
capital intensive group (positive relationship). While debt ratio was the only variable that affects
ROA (negative relationship). These results showed that regardless of the type of company, whether
working capital or fixed capital intensive, managing working capital properly is equally important.
Moreover, managing inventory as well as cash conversion efficiency to an optimum level will yield
more profit in the working capital intensive type of company, while two other different variables
create more profit in fixed capital intensive type of the company
Deloof,(2003) have investigates relationship between working capital management and corporate
profitability for a sample of 1009 large Belgian non financial firm for the period 1992-1996. The
result from the analysis showed that there was a negative relationship between profitability that
measure by gross operating income and cash conversion circle as well as number of days accounts
receivable and inventories. He suggested that mangers can increase corporate profitability by
reducing the number of day’s Accounts receivable and inventories less profitable firms waited
longer to pay their bills.
Lazaridis,I. and Trynidis,D. (2006) have also investigate the relationship between working capital
management and profitability of listed company in the Athens Stock Exchange. A sample of 131
listed companies for a period of 2001- 2004, was used to examine this relationship. The result from
regression analysis indicated that there was a statistical significance between profitability measured
through, operating profit and the cash conversion cycle. From those results they claimed that the
managers could create value for shareholders by handling correctly the cash conversion cycle and
keeping each different component to an optimal level.
Singh and Pandey (2008) had an attempt to study the working capital component and the impact of
working capital management on profitability of Hildalco industries limited for a period from 1990 to
2007. Results of the study showed that current ratio liquid ratio, receivables turnover ratio and
39
working capital to total assets ratio had statistical significant impact on the profitability of
Hinssdaico industries Ltd.
Raheham and Nasr (2007) have selected a sample of 94 Pakistani firms on Karachi stock exchange
for a period of 6 years from 1999- 2004 to study the effect of different variables of working capital
management on the net operating profitability. From the result of the study, they showed that there
was a negative relationship between variables of working capital management including the average
collection period, inventory turnover in days cash conversion cycle and profitability. Besides, they
also indicated the size of the firm. Measured by natural logarithm of sales and profitability had a
positive relationship.
Afza and Nazir (2009) made an attempt in order to investgate the traditional relationship between
working capital management policies and a firms profitability for a sample of 304 non – financial
firms listed on Karachi stock exchange (KSE) for the period 1998 – 2005 they study found
significant difference among their working capital different industries moreover, regression result
found a negative relationship between the profitability of firms and degree of negative relationship
between the profitability of firms and degree of aggressiveness of working capital investment and
financing policies. They suggested that manager could create values if they adopt a conservative
approach toward working capital investment and working capital financial policies
Amit Mallik, Debashish and Debdas (2005) in the study regarding the relationship between working
capital and profitability of Indian pharmaceutical industry found and concluded that no definite
relationship could be established between liquidity and profitability.
Vishanani and Shah (2007) study the impact of working capital management policies on corporate
performance of Indian consumer Electronic industry by implemented simple correlation and
regression models. They found that no established relationship between liquidity and profitability
exist for depicted different type of relationship between liquidity and profitability although majority
of the companies revealed positive association between liquidity and profitability.
Lyrondi and Lazardis (2000) investigate the cash conversion cycle and liquidity position of the food
industry in cycle as a liquidity level indicator of the food industry in Greece and tried to determine its
relationship with the traditional liquidity measurement and profitability measurement on return on
investment, return on equity and net profit margin, they found significant, positive relationship
between cash conversion cycle and payable deferred period. The relationship between liquidity
measurement variables and profitable measurement variable was not statistically significant and there
was no relationship between cash conversion cycle and leverage ratio. To determine the solvency level
of firms according to existing obligation of firms different techniques may apply as measurement of
liquidity Current ration, quick ratio and cash ratio are among the most traditional liquidity
measurement techniques and the most recent dynamic techniques, cash conversion cycle is applied for
40
measurement of liquidity level of firms. The relationship of these traditional and modern liquidity
measurement techniques are studies by Lyroudi and MC Carty (1993) for small U.S companies for the
period 1984 – 1988 and they found that cash conversion cycle was negatively related with the study
revealed difference between current ratio but positively related with quick ration. In addition, the
study revealed difference between the concept of cash conversion cycle in manufacturing retail,
wholesale and Service industries. The advantage of using modern liquidity measurement technique is
that it will help to evaluate working capital change and it facilities the monitoring and controlling of
its components, receivable inventories and payable. The smaller value of cash conversion cycle shows
that, the quicker the firms can recover cash from sales of finished products and the more cash will
have hence this will lead to have more liquid assets by firms. If cash conversion is high, it will take
longer time recover cash, thus high cash conversion cycle implied an existence of problem in liquidity,
lyroudis and lazardis (2000)
Mukhopadhyay (2004) states that firms are badly constrained to smoothly run the day-to-day
operation if there is negative working capital and also difficult to settle short term obligation..
Singh (2004) states also that the liquidity position of any firm mainly depends upon accounts receivable
collection and payable deferred policy as well as inventories conversion period of firms.
Kim, Mauer and Sherman (1998) examine the determinants of corporate liquidity of 915 U.S industrial
firms for the period 1975 to 1994 by using panel data and different models. They found that firms with
large market to book ratio have significantly large position in liquid assets. In addition firm size tends to
be negatively related to liquidity. Their, finding revealed that positive relationship between liquidity and
cost of external financing to the extent that market to book ratio and firm size are reasonable proxies for
the cost of external financing. They also found out that firm with more volatile earning and lower return
on physical assets relative to those liquid assets lead to have significantly large position in liquid assets.
Enyi (2005) studies the relative solvency level of 25 sample firms. The finding of the study revealed
that the gap created by the inability of traditional liquidity measurement of solvency level, like current
ratio quick ration and other solvency ratio, to effectively determine the proper size or volume of
working capital is fulfilled by the relative solvency level model. In addition, the study revealed that the
firms with adequate working capital related to their operational size have performed better than firms
which have less working capital in relation with their operational size
Mehar (2001) studies the impact of equity financing on liquidity of 255 firms listed in Karachi Stock
exchange for the period 1980 – 1994 by using a pooled data. The finding of the study depicted that
equity financing plays an important role in determining the liquidity position of firms. From this finding
it is concluded that equity and fixed assets have positive relationship with working capital, in the long
term, however the liquidity position will be deteriorated with the increases in paid up capital.
41
Hsiao and Tahmisciaglu (1997) in their study reveal that liquidity may be affected by substantial
difference across firms in their investment behavior and firms characteristics.
Bhunia (2007) studies liquidity management of public sector iron and steal enterprises in India.
He has found out that the actual values of working capital lower than the estimated value of
working capital for both companies under study and poor liquidity position in case of both
companies.
Eljelly (2004) elucidates that efficient liquidity management involves planning and controlling
current assets and currents liabilities in such a manner that eliminates the risk of inability to meet
due short term obligation and avoids excision investment in these assets. Then relationship
between profitability and liquidity was examined, as measured by current ratio and cash gap
(cash conversion cycle) on a sample of joint stock companies in sauid Arabia using correlation
and regression analysis. The study found that the cash conversion cycle was of more importance
as a measure of liquidity than the current ratio that affect on profitability. The size variable was
found to have significant effect on profitability at the industry level. The result were stable, and
had important implication for liquidity management in various Saudi companies. First, it was
clear that there was a negative relationship between profitability and liquidity indicators such
current ratio and cash gap in the Saudi sample examined. Second, the study also revealed that
there was great variation among industries with respect to the significant measure of liquidity.
Ghosh and Maji (2004), in their paper made attempt to examine the efficiency of working capital
management of the Indian cement companies during 1992 – 1993 to 2001 -2002. For measuring
the efficiency of working capital management, performance utilization, and overall efficiency
indices were calculated instead of using some common working capital management ratios.
Setting industry norms as target – efficiency levels of the individual firm, this paper also tested
the speed of achieving that target level of efficiency by an individual firm during this period of
study. Finding of the study indicated that the Indian cement industry as a whole did not perform
remarkably well during this period.
Shin and soenen (1998) highlightes that efficient working capital management (WCM) was very
important for creating value for the shareholders. The way working capital was managed had
significant impact on both profitability and liquidity. The relationship between the length of net
trading cycle, corporate profitability and risk adjusted stock return was examined using
correlation and regression analysis, by industry and capital intensity. They found a strong
negative relationship between lengths of the firms net trading cycle and its profitability. In
addition shorter net trade cycle were associated with higher risk adjusted stock returns.
Smith and Begemann (1997) emphasizes that those who promoted working capital theory shared
that profitability and liquidity comprised the salient goals of working capital management. The
42
problem arose because the maximization of the firm’s returns could seriously threaten its
liquidity, and the pursuit of liquidity had a tendency to dilute returns. This article evaluated the
association between traditional and alternative working capital measure and return on investment
(ROI) specifically in industrial firms listed on the Johannesburg stock Exchange (JES). The
problem under investigation was to establish whether the more recently developed alternative
working capital concepts showed improved association with return on investment to that of
traditional working ratios or not. Results indicated that there were no significant differences
amongst the years with respect to the independent variability in return on investment (ROI). The
statistical test results showed that a traditional working capital leverage ratio, current liabilities
divided by funds flow, displayed the greatest associations with return on investment well – know
liquidity concepts such as the current and quick ratios registered insignificant associations while
only on the newer working capital concepts, the comprehensive liquidity the comprehensive
liquidity index, indicated significant associations with return on investment.
Ganesu (2007) in his study examines working capital management efficiency of firms from
telecommunication equipment industry. The relationship between working capital management
efficiency and profitability was also examined using correlation and regression analysis.
(ANOVA) analysis was done to study the impact of working capital management on profitability.
A sample of 443 annual financial statements of 349 telecommunication equipment companies
was used, covering the period 2001 – 2007, this study found evidence that even though day
working capital is negatively related to the profitability of firms in the telecommunication
equipment industry.
Howorth (2003) in his study on the field of working capital management focuses on the routines
employed by firms. The research showed that firms which focus on cash management were
larger, with fewer cash sales, more seasonality and possibly more cash flow problems. While
smaller firms focused more on stock management and less profitable firms were focused on
credit management routine. It was suggested that high growth firms follow a more reluctant
credit policy towards their customers, while they tie up more capital in the form of inventory.
Account payables will increase due to better relations of suppliers with financial institutions
which divert this advantage of financial cost to their client (Peterssen and Rajan 2007). Falope
and Ajilore (2009) examined the working capital management and corporate, profitability;
Evidence from panel data: analysis of selected quoted companies in Nigeria. They used the
sample of Nigerian quoted non-financial firms for the period 1996-2005. The study found a
significant negative relationship between net operating profitability and the average collecting
period inventory turnover in days, average payment period and cash conversion cycle for a
sample of fifty Nigerian firms listed on the Nigeria stock Exchange. Furthermore, the study
43
found no significant variation in the effects of working capital management between large and
small firms. These result suggest that management can create value for their working capital in
more efficient way by reducing the number of day accounts receivable and inventories to a
reasonable minimum.
Muchina and Kiano (2011) in their study analyzes the influence of working capital management
on firms’ profitability in Kenya. They used fixed panel data of 232 firms. The result indicated
that the average debtor day, stock turnover period and the cash conversion cycle are significantly
affecting the profitability of the firms. They found out also that the manufacturing firms are in
general facing problems with their collection and payment policies. Moreover, the financial
leverage, ratio of current asset to current liability and firm size also have significant effect on the
firm profitability. The study also concluded that SMES in Kanya are following conservative
working capital management policy and payment policy. They suggested that the effective
polices must be formulated for the individual component of working capital and that efficient
management and financing of working capital (current assets and current liabilities) can increase
the operating profitability of manufacturing firms. For efficient working capital management,
specialized persons in the field of finance should be hired by the firms for expert advice on
working capital management in the manufacturing sector.
Mccarty and Lyroudi (1993) studied on small U.S companies for the period 1984 – 1988, and
they found out that cash conversion cycle was negatively related with current ratio but positively
related with quick ratio. In addition the study revealed differences between the concept of cash
conversion cycle in manufacturing retail, wholesale and service industries.
Ching et al (2011) in their study investigates the difference between corporate profitability and
working capital management in two separate groups of companies; working capital intensive and
fixed capital intensive, and to identify the variables that most affect Profitability. The
profitability was measured in three different ways: return on sales (ROS), on asset (ROS) and on
equity (ROE). The independent variable used are cash conversion efficiency, debt ration day
working capital, days receivable and day’s inventory. Two samples were obtained consisting of
16 Brazilian listed companies in each group for the period 2005 – 2009. Multiple linear
regression used identified that as far as ROS and ROA are concerned, to manage working capital
properly is equally relevant for the two groups of companies. They found out that the impact of
debt ratio and days of working capital are relevant in the company profitability in the fixed
capital group as opposed to the working capital group. From ANOVA it was evident that days
inventory has negative relationship with ROS and ROA but has no statistical evidence in ROE
improvement in working capital intensive group. They had also identified days of working
capital as the variable that influences ROS in the second group (positive relationship) while debt
44
ratio was the only variable that affects ROA (negative relationship) these result show that
regardless the type of company whether working capital or fixed capital intensive, managing
working capital properly is equally important; Moreover, managing inventory as well as cash
conversion efficiency to an optimum level would yield more profit in the working capital
intensive type of company, while two other different variables created more profit in the fixed
capital intensive type of company.
Padachi (2006) in his study also studies on the trends in working capital management and its
impact on firms’ performance: analysis of Mauritian small manufacturing firms, to identify the
causes for any significant difference between the industries. The dependent variable return on
total assets is used as a measure of profitability and the relation between working capital
management and corporate profitability was investigated for a sample of 58 small manufacturing
firms, using panel data analysis for the period 1998-2003. The regression result shows that high
investment is inventories and renewable is associated with lower profitability. The key variable
used in the analysis was inventories days, accounts receivables days, accounts payable days and
cash conversion cycle. A strong significant relationship between working capital management
and profitability has been found in pervious empirical work. An analysis of the liquidity,
profitability and operational efficiency of the five industries trend in the short – term component
of working capital financing.
Mathuva (2009) examines the influence of working capital management components on
corporate profitability by using a sample of 30 firms listed on the Nairobi stock Exchange (NSE)
for the period 1993-2008. He used Pearson and spearman’s correlations, the pooled ordinary lest
square (OLS), and the fixed effects regression models to conduct data analysis. They finding of
his study were:
i. There exits a highly significant negative relationship between the time taken for firms to
collect cash from their customers (account collection period) and profitability,
ii. There exists a highly significant relationship between the period taken to convert inventories
into sales (the inventory conversion period) and profitability, and
iii. There exits a highly significant positive relationship between the time it takes firm to pay its
creditor (average payment period) and profitability.
Gill et al (2010) seek to extend Tryfonidis findings regarding the relationship between working
capital management and profitability. A sample of 88 American firms listed on New York stock
Exchange for a period of 3years from 2005-2007. They found statistically significant relationship
between the cash conversion cycle and profitability,
Irfan (2011) The study ingestigates the impact of working capital on the performance of the firm
using a sample of 253 non financial listed companies of Karachi stock exchange (KSE), Pakistan,
45
the study used secondary data taken from Balance sheet Analysis of stock listed companies on
KSE published by state Bank of Pakistan. Result were analyzed by using the logistic Regression,
OLS Regression and Pearson correlation techniques. The result suggests that out of the five
selected components of working capital management only current asset over total sales showed
significant negative relationship with both the proxies of performance i.e return on equity and
return on assets. While current assets over total assets (CATA), inventory turnover, debtors
turnover and current ratio showed significant positive relationship with performance. Logistic
regression result suggested that probability of firm being in profit is highly determined by
CATA, CATS and CR.
Mohammad (2011) studied a sample of 1063 companies listed on Tehran stock exchange to study
the relationship between working capital and corporate profitability. He found that this is
negative relationship between number of days accounts receivable and profitability.
Anup (2007) examines the political and economic impacts of working capital management. He
concluded that pharmaceutical firms operated in Bangladesh are efficiently dealt with their
liquidity preference and investment criteria and this is due to the competitive nature of the
industry.
Ashraf (2o12) studied a sample of the 16 Indian firms, on BSE including firms from different
sectors of our economy for a period of 2006 – 2011. He examined the effect of Debt ratio,
average collection period inventory turnover in days, Average payment period, cash conversion
cycle and current ratio on the wet operating profitability of sample firms. Descriptive and
regression are used for analysis. The results show that there is a strong negative relationship
between variables of the working capital management and profitability of the firms except the
sales (size of the company). We also find that, there is a significant negative relationship between
debt used by the firm and its profitability.
In Bieniasz (2011) The result the Study proved that in the food industry sectors with the shortest
working capital cycles, relatively higher rate of profitability were obtained. A favourable
influence of working capital cycles reduction of the profitability was also verified by means of a
multiple regression analysis.
In the study of Yeboah and Kwaku (2010) It was found out that cash position of banks, creditors
payment period and profitability have significantly positive relationship, with the cash position of
bank in Ghana
Takon S.M (2013) in his study effect of working capital management on firm profitability in
selected quoted companies, selected two companies each from the 28 NSE sector classifications.
He used panel data and Generalized least square fixed effect regression, he found out that
liquidity has a positive and significant relationship with ROA Age has position significant
46
relationship with profitability. He also observed that CCC has negative and significant
relationship with profitability and that all receivables has negative significant relationship with
profitability
Olugbega (2010) carried out a study on the appraisal of the relationship between working capital
and liquid Assets of Nigerian companies. A comparative study of ten selected companies.
Specifically, the study seeks to find out whether most Nigerian companies suffer from
inadequacy of liquid assets to meet their short term financial obligations. To determine this
relationship descriptive approach was adopted coupled with the use of correlation coefficient to
establish the nature of the relationship. He recommended that companies should strive to
maintain optimal level, short term bank facilities should be a last resort, and companies are
encouraged to exploit more cost – effective, finding rights issue to raise the needed, working
capital.
Karaduman,H.A et al, (2010), in their study “Effects of working capital management on
profitability; the case of selected companies in the Istabul stock Exchange (2005 – 2008) stated
that working capital management in one of the essential determinants of firm market value
because it directly affects profitability. They went on to say that firms should establish a fine
balance between profitability and risk when it comes to managing working capital. The paper
mainly aimed to provide some empirical evidence on the effects of working capital management
on the profitability of selected companies in the Istanbul stock Exchange. The panel data
methods were employed in order to analyze the unquestionably influence the companies in the
ISE. The findings were similar to the previous studies of Deloof (2003), Lazaridis and tryfondis
(2006), Gracia – Tenienl Martineg – Solano, (2007) and Zariyawati et al, (2009).
2.5 Summary of Literature Review
The review of extant literature reveals a large number of studies examining working capital
management and profitability in different countries, including Nigeria. Ching et al, (2011), in
their study used return on sales and on equity to measure profitability. And their independent
variables include cash conversion efficiency debt ratio, days receivables and days inventory days,
accounts receivable days, accounts payable days and cash conversion cycle as variables. He
found out that there was a strong significant relationship between working capital management
and profitability. Takon, (2013) found out that liquidity has a positive and significant relationship
with ROA. Lyrondi and Lazardis, (2000), found out in their study that cash conversion cycle was
relatively related with the study revealed differences between current ratio but positively related
with quick ratio. Vishanani and shah (2007) found out in their study that no established
47
relationship exists between liquidity and profitability although majority of the companies
revealed positive association between liquidity and profitability. Amit, Mallik and Dabdas,
(2005) found and concluded in their study that there is no definite relationship established
between liquidity and profitability.Sharma and Kumar[2011], in their study revealed that
inventory number of days and number of days accounts receivable and cash conversion-period
exhibit a positive relationship with corporate profitability.These studies are not in consistent with
this study based on this, the researcher deemed it necessary to fill this gap.
48
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53
CHAPTER THREE
RESEARCH METHODLOGY
3.1 Research Design:
Since there are so many types of research design, the one that was used in this study is the ex-post factor
research design. This is because, according to Onwumere, (2009), it involves events that have already taken
place in the past. The records that were observed are from 2000-2011 a period of twelve years. The variables
that will be tested in the studied firms are accounts receivable, accounts payable, inventories, cash
conversion cycle, Return on total assets and liquidity. The study will cover the period 2000-2011.
3.2 POPULATION AND SAMPLE SIZE
The population of this study is all the manufacturing companies quoted in Nigeria Stock Exchange (NSE).
However, because of unavailability of Data, the study used only 22 manufacturing companies. The number
of the population is 53[see appendix 1]. Only 22 companies where studied because of incomplete and
unavailability of data [see appendix 2]. Manufacturing sector was chosen because it remains the most
powerful engine for economic structure of countries (Jide, 2010). This is because any country that engages
in only trading (buying and selling) and not producing goods may face doom in the future. Our decision to
use the manufacturing sector could also be explained by the nature of their asset mix, for instance,
manufacturing involves inventories, working progress among others, unlike some other sectors of the
economy.
3.3 NATURE AND SOURCES OF DATA
The study used only secondary data that were extracted from the Annual Reports and statements of Account
of the selected manufacturing companies. The data from the Annual Report are reliable, because according
to part X1, chapter one of the companies and Allied Matters Decree 1990, Companies are required to keep
accounts and to produce accounts that give true and fair view of the company. Companies are required to
prepare the balance sheet, profit and loss account, name of directors and their reports, Auditors Report, and
they must be published. Based on this, this study uses Annual Reports and statements filed in the Nigeria
stock exchange. The data for this study include the turnover, receivables , payables, stocks, cash, profit
before tax, sales, purchases and assets.
3.4 Description of research variables
The choice of research variables is primarily guided by previous empirical studies along this line. Thus, the
variables are defined to be consistent with those of Teruel and Solano (2005), Deloof (2003), Shin and
Soenen (1998) and Karaduman et al (2011).
54
3.4.1 Dependent Variable (Profitability)
The dependent variable in the study is firm’s profitability. In order to analyze the effect of working capital
management on the firm’s profitability, the return on assets will be used as dependent variable. This is
because the return on assets (ROA) is an indicator of managerial efficiency. (Lazaridis and trynids, (2006),
Delof (2003), Shin and Soenen (1998) Falope and Ajilore, (2009), Singh and Pandey, (2008) and
Karaduman et al (2011).
PBT
Profitability Total assets…………………………………………………….3.1
3.4.2. Independent Variables
With regards to the independent variables, working capital management is measured by using
accounts receivable ratio, stock turnover ratio, accounts payable ratio, the cash conversion cycle ratio (CCC)
and Liquidity ratio (Ching et al 2011).
3.4.2.1 Accounts Receivables Ratio.
Accounts receivables are customers who have not yet made payment for goods or services, which the
firm has provided. The objective of debtor management is to minimize the time-lapse between completion of
sale and receipt of payment. In this respect accounts receivable ratio (AR) is calculated as accounts
receivable/sales. This variable represents the receivable that the firm will collect from its customers.
(Basley and Bringham 2005, Samilogu and Demirqunes 2008, Falope and Ajilore 2009, and Sharma and
Kumar 2011)
The above authors examined the influence accounts receivable have on profitability in their different
countries
Accounts Receivable ratio – Receivables…….……………3.2 Sales
3.4.2.2 Stock turnover/Inventories ratio.
Inventories are list of Stock-raw materials, working-in-progress or finished goods waiting to be
consumed in production or to be sold. Inventory ratio (INV) is calculated as inventories/Purchases or cost of
sales. This variable represents the rates stocks are held by the firm. Longer storage represents a greater
investment in inventory for a particular level of operation. (Chariton et al 2010 Ghosh and Maji 2004,
Samiloglu and Demirqunes 2008, Muchina and Kiano 2011, Falope and Ajilore 2009 and Ching et al 2011).
Stock turnover/Inventories ratio – Inventories ……………………………3.3 Purchases/cost of sales
3.4.2.3 Accounts Payable ratio.
Accounts payable are suppliers whose invoices for goods or services have been processed but who
have not yet been paid. Organizations often regard the amount owing to creditors as a source of free
55
credit. Accounts payable ratio (AP) represents the rates of payables of firms to their suppliers.
Accounts payable ratio is calculated as accounts payable/purchases or cost of sales. The higher the
value, the longer firms take to settle their payment commitment to their suppliers. (Singh 2004,
Adina 2010, Chariton et al 2010, Kwasi 2010, Singh and Pandy 2008, and Raheman and Nasr 2007).
Accounts Payable – Payables …………………………………….3.4
Purchases/cost of sales
3.4.2.4 Cash Conversion Cycle ratio (CCC)
The cash conversion cycle (CCC) is a proxy for working capital management efficiency. It is rate
cash flows from the suppliers to inventory to accounts receivable and back into cash. It is therefore
an additive measure of funds that are committed ie tied inventories and receivables less payments
that are deferred to suppliers. It has been interpreted as the cash outlays that arise during the
production of output and the cash inflows that result from the sale of the output and the collection of
the accounts receivable. The CCC is calculated by subtracting the payables and the inventories from
the receivables. (Meearty and Lyroudi 1993, Gill et al 2010, Karaduman et al 2011, Kwasi 2010,
Deloof 2003).
Cash conversion Cycle = Receivables - (payables and Inventories) …………………3.5
3.4.2.5. Liquidity ratio.
Liquidity management is necessary for all businesses, small or large. Because, it means collecting
cash from customers so that having no difficulty in paying short term debts will be achieved.
Therefore, when a business does not manage its liquidity well, it will have cash shortages and will
result in difficulty in paying obligations. As a result, in addition to profitability, liquidity
management is vital for ongoing concern, corporate liquidity is examined from two dimensions:
static or dynamic view (Lancaster et al, 1999, Fairs and Hutchison 2002, and Moss and Stine,1993].
The Static view is based on commonly used traditional ratios, such as current ratio and quick ratio,
calculated from the balance sheet amounts. These ratios measure liquidity at a given point in time,
whereas Dynamic view measure on going liquidity from the firms operations. As a dynamic measure
of the time it takes a firm to go from cash outflow to cash inflow which is measured by cash
conversion cycle.
The two key ratios that can be calculated to provide a position of a business are:
⇒ Current ratio
⇒ Acid test (quick) ratio
Current ratio = Current Assets Current Liabilities
Quick ratio= current asset - inventories Current liabilities
56
Liquidity:- =current assets……………………………3.6 Current liabilities
3.5 TECHNIQUE FOR ANALYSIS Accounts receivable, Accounts payable, inventory, cash conversion circle, liquidity, debt and sales
growth are the independent and control variables. Generalized least square Multiple regression
technique is used to measure the impact the independent and control variables have on the dependent
variable. The study examined the effect of working capital management as a measure to profitability.
With firm year records, the study applies the multiple regression models to test the various
hypotheses. This helps to express the functional relationship between managers and acquisitions and
the variables (Higiris, 2005) Pearson correlation statistical tool can only help in analyzing the
relationship between only two variables. If for instance, Correlation is used to study why people
receive the compensation they do, but you cannot use it to study how a person’s current
compensation is related to both their education and how long they have worked for the company.
Multiple regression analysis is a statistical tool for understanding the relationship between two or
more variables, it allows for much more flexibility. Since we know that life is so complicated that it
takes way more than two variables to even begin to explain/predict why things are the way they are
and a new tool is needed i.e. multiple regression statistical tool.
This tool allows us to examine how multiple independent variables are related to a dependent
variable. Once you have identified how this multiple variables relate to your dependent variable, you
can take information about all of the independent and control variables and use it to make much
more powerful and accurate predictions about why things are the way they are. This process is
known as multiple regressions. Multiple regression is very advanced statistical tool and it is
extremely powerful when you are trying to develop a “model” for predicting a wide variety of
outcomes. It is more amenable to ceteris paribus analysis because it allows us to explicitly control for
many other factors that simultaneously affect the dependent variable. This is important both for
testing economic theories and for evaluation policy effect when we must rely on non-experimental
data. Multiple regression models can accommodate many explanatory variables that may be
correlated, we can infer casualty in cases where simple regression analysis would be misleading. It
can also be used to build better models for predicting the dependent variable. Since return on total
Asset will be used to measure dependent variable (Profitability of the study and the independent
variable which are; Accounts receivables, Accounts payable, Inventory, Cash Conversion Circle, and
Liquidity. Multiple regression technique is used to measure the effect the independent variables have
on the dependent variable
Y = B0 + B1 + B2 …………..B5 + Ui
57
3.6 Model Specification In this study, the independent and dependent variables are used into an equation called multiple
regressions. To express the model of multiple regressions in equation modified to suit the respective
hypotheses. This study is a time series study that covers 2000 – 2011.
Y = B0 + B1 + B2 …………..B5 + Ui ……………………………………..3.7 Where,
Y= profitability
B1 = Accounts payable (AP)
B2 = Accounts Receivable (AR)
B3 = Cash Conversion cycle (CCC)
B4 = Stock Turnover (STO)
B5 = Liquidity (LQ)
Bo = the intercept of the regression line,
U1 = the error term
To test the competing views on the (accounts payable, accounts receivable, cash conversion cycle,
stock turnover and liquidity) in Nigeria, we modify the multiple linear regression in equation (3.7)
Profitability = B0 + B1(AP) + B2 (AR) + B3 (CCC) + B4 (STO) + B5 (LQ) + Ui ……….3.8 Where, profitability is financial performance, AR is accounts receivable, AP is accounts payable,
CCC is each conversion cycle, ST is stock turnover and LQ is liquidity.
To ascertain the net impact of working capital management on the corporate profitability in Nigeria,
we will control other variables that might impact on profitability. The controlled variables are debt
leverage and sales. The controlled variables are debt leverage as a ratio to total asset and are proxy
leverage while sales is measured as a decrease or increase of the annual sales as a percentage of
sales. Thus equation (3.8) is written as
Profitability=Bo +B[AP] B2(AR)+B3(CCC]+B1[STO] B3(LQ)ii + B2DT(control)2i +
B2SL(control)2i + Ui ……………………………………………..3.9
Where DT is Debt/Leverage as a ratio of total Assets and SL is sales as a percentage of decrease or
increase of the annual sales. The same multiple regression will be used to estimate the profitability
model is (3.9).
3.7 Computing the Multiple Regression Analyses
First, values of critical indices in the management of the working capital of some twenty two
manufacturing firms in Nigeria obtained from Nigeria Stock Exchange were recalculated using the
formulae listed in 3.6 above to achieve the final data used for this study. Secondly the computed data
were further subjected to multiple regression analysis. In analyzing the computed data for the
variables involved in the study, it was necessary to employ four functional models of multiple
58
regression in order to determine and select the model that best fitted the analysis. Thus the four
multiple regression models employed in the analysis include the linear, semi log, double log and
exponential regression models. They are implicitly expressed as follows:
a) Linear regression model:
Profitability= Bo + B1(AR) + B2(STO) + B3(AP) + B4(CCC) + B5(LQ) +
B6(DT) + B7(SL) + Ui…………………………………………………..3.10
b) Semi log regression model:
Profitability= LogBo + LogB1(AR) + LogB2(STO) + LogB3(AP) +
LogB4(CCC) + LogB5(LQ) + LogB6(DT) + LogB7(SL) + ……3.12
c) Double log regression model:
Log Profitability= LogBo + LogB1(AR) + LogB2(STO) + LogB3(AP) +
LogB4(CCC) + LogB5(LQ) + LogB6(DT) + LogB7(SL) + ……3.13
d) Exponential regression model:
LogProfitability= Bo + B1(AR) + B2(STO) + B3(AP) + B4(CCC) + B5(LQ) +
B6(DT)+ B7(SL) + Ui…………………………………….3.14
After obtaining the results of the four functional multiple regression models, decisions were
therefore taken on which among them should be chosen as the best fit model in the analysis. The
choice models were then used in the interpretation of the results. Decision and choice of the best fit
model were fundamentally based on the following: a) the one with highest number of significant
variables b) significance of F-ratio which measures the fitness of a model in using the independent
variables to explain the dependent variable c) the magnitude of the coefficient of multiple
determinations (R2). Although decisions on the choice of models were based mostly on ones with
highest number significant variables, result of the analysis must necessarily show significant F-ratio.
The coefficients of multiple determination (R2) were employed in the study to quantify extent of
variation in the dependent variable (profitability ratio) caused by the explanatory (independent)
variables considered in the study. Furthermore, the analysis were conducted at 1%, 5% and 10%
levels of significance respectively denoted as ***, ** and * signs against the coefficient values in the
result tables presented in Chapter four. Again, the twenty two manufacturing firms were grouped into
seven sectors and computed data for firms that belong to each sector were pooled, analyzed and
presented as representative of the sector. Also, computed data for all twenty two manufacturing firms
considered in the study were equally pooled, analyzed and presented differently as representative of
all manufacturing firms in Nigeria. Results of data analyses conducted are presented in Chapter four.
However, some hypotheses were also set for the study. These include:
Hypotheses one – Accounts receivable has no significant effect on profitability,
59
We have Receivable/Sales……….…………………...............3.11 Hypotheses two – Accounts payable has no significant effect on profitable, We have Payable/purchases…………………………………3.12
Hypotheses three – there is no significant relationship between stock turnover and firm profitability
We have Inventories/purchases……………………………………….3.13
Hypotheses four – there is no significant effect of cash conversion cycle on profitability of the
Nigerian quoted manufacturing firms,
We have Accounts Receivables - (Accounts payables and inventory)
………..………………………………………………………………3.14
Hypotheses five – there is no significant relationship between liquidity and profitability of the
Nigeria quoted manufacturing firms.
We have Liquidity:- = Current Assets……………3.15 Current liability
60
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62
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.1 INTRODUCTION:
This chapter presents and analyses the descriptive statistics, and also the multiple Regression of the
Dependent, Independent variables and control variables. Statistical averages and standard deviations of the
variables are compared for the whole samples, as well as basic working capital components such as accounts
payable, accounts receivable, stock turnover/inventory cash conversion cycle and liquidity. Statistical
averages and standard deviations of the variables are also compared across industries in order to establish
industrial pattern of working capital management in Nigeria. The main aim is to draw certain conclusion on
working capital management of quoted companies in Nigeria. Another aim is to highlight the trend of
working capital management of quoted firms in Nigeria within the period under study.
The dependent variable for the study is profitability measured with profit before tax while the independent
variables comprise of accounts payable, accounts receivable, stock turnover, cash conversion cycle and
liquidity, and the control variables include Sales Growth and Debt. The researcher stopped some firms from
the final observation due to non-availability of data on key variables. The data are presented along industrial
patterns based on the Nigerian stock exchange sector classification (manufacturing)
Table 4.1 Below presents data for return on asset, accounts receivable, stock turnover, accounts payable, cash conversion cycle, liquidity and debt ratios as well as the sales growth for 7-Up Nigeria Plc.
Table 4.1.1: Raw Data for 7-Up Nigeria Plc. years Return on
Asset ratio Accounts Receivable ratio
Stock Turnover ratio
Accounts Payable ratio
Cash Conversion Cycle
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.113106 0.104605 0.355011 0.539652 -0.79006 1.03707 0.022355 0 2001 0.145218 0.099151 0.345423 0.475622 -0.72189 1.140194 0 19.69397 2002 0.271914 0.104627 0.293899 0.046028 -0.2353 1.21306 0.056991 46.30892 2003 0.218926 0.085761 0.311998 0.572622 -0.79886 1.073494 0.053993 20.13747 2004 0.160043 0.11591 0.309017 0.490233 -0.68334 1.178816 0.020987 5.029189 2005 0.108646 0.092035 0.305505 0.63048 -0.84395 0.986985 0.049627 16.12928 2006 0.099763 0.089625 0.277554 0.576789 -0.76472 1.122865 0.055393 27.23907 2007 0.090575 0.10751 0.258471 0.444438 -0.5954 1.330172 0.207219 23.72896 2008 0.103443 0.104442 0.226578 0.072584 -0.19472 1.442658 0.244824 11.94874 2009 0.069744 0.117126 0.24219 0.078429 -0.20349 1.143692 0.230766 14.03912 2010 0.078634 0.102726 0.298402 0.122955 -0.31863 0.992808 0.178027 17.79708 2011 0.062763 0.082543 0.254755 0.096812 -0.26902 1.057806 0.189346 24.4201
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
The return on asset captures the profitability of the firm. The firm at the beginning of the period earned 0.113 or 11.3% return on it’s asset in year 2000. This increased to 14.5% in 2001 while recording the highest increase during the period of 0.2719 or 27.19% in 2002. After this period the return on asset for the
63
firm recorded declines such that in 2006, the return on asset for the firm stood at 0.099 or 9.97% while increasing to 0.1034 or 10.34% in 2008 while ending the period in 2011 at 0.0627 or 6.27%. Seven-Up Nigeria Plc recorded the highest accounts receivable of 0.1171 or 11.71% for the period in 2009 an increase from 10.46% in year 2000. The Firm’s accounts receivable dropped to 0.1027 or 10.27% and 0.08254 or 8.25% in 2010 and 2011 respectively while recording the least accounts receivable of 0.08254 or 8.25% in year 2011 the ending of the period. The stock turnover ratio for the Firm recorded varying degrees of increases and declines during the period. The Firm recorded the highest stock turnover of 0.355011 or 35.50% in the beginning of the period in 2000 while recording the lowest stock turnover ratio of 0.22657 or 22.65% in 2008. However, the stock turnover ratio for the Firm increased afterwards to 0.2984 or 29.84% while ending the period at 0.2547 or 25.47% in 2011. The accounts payable ratio for Seven-Up Nigeria Plc started the period in year 2000 at a ratio of 0.5396 or 53.96%. The highest accounts payable ratio of 0.63 or 63% during the period was recorded in year 2000 the beginning of the period while recording the least ratio of 0.0460 or 4.60% in year 2002. At the end of the period in 2011, the accounts payable ratio for the Firm stood at 0.09681 or 9.68%. For cash conversion cycle which captures the length of time it takes the Firm to commit cash into raw materials to the time of selling finished products and receiving cash is desirous of a negative ratio at each point in time. This implies that the shorter the cycle, the better it is for the Firm and table 4.1 above reported negative ratios for the Firm all through the period of the study. The liquidity ratio for the Firm fail though at above the ratio of 1:1 in most years during the study period is lower than the ideal current ratio of 2:1. The highest liquidity ratio of 1:44 is recorded in year 2008 while the least liquidity ratio of 0:99 is recorded in year 2010. However, the liquidity ratio for the firm ended the period of at the ratio of 1:05 in 2011. The debt ratio for the Firm increased from a meager 0.055 or 5.5% in 2006 to 0.2072 or 20.72% in 2007. The highest debt ratio of 0.2448 or 24.48% was recorded in 2008 implying that the Firm used more debt to finance it’s activities in year 2008. However, the least debt ratio of 0.0209 or 2.09% debt to equity ratio was recorded in year 2003 while equity to debt ratio of 0.1893 or 18.93% ended the period of the study in 2011. The sales growth rate for the firm recorded the highest rate in growth of 46.30% in year 2002 while the least growth rate of 5.02% in year 2004. The growth rate for the Firm ended the period in 2011 at 24.42%. Table 4.1.2: Raw Data for Cardbury Nigeria Plc. Years Return on
Asset ratio
Accounts Receivable ratio
Stock Turnover ratio
Accounts Payable
Cash Conversion Cycle
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.200304 1.699728 0.569838 0.930822 0.199068 1.165491 0.001468 -80.1463 2001 0.225111 0.105478 0.314446 0.134515 -0.34348 1.856179 0.233933 30.41862 2002 0.257688 0.238158 0.280602 0.195981 -0.23842 1.763929 0 21.04079 2003 0.078091 0.250475 0.254396 0.129152 -0.13307 1.861905 0 28.48299 2004 0.184423 0.17217 0.408579 0.203577 -0.43999 1.417961 0 7.661647 2005 0.120165 0.306413 0.294346 0.205678 -0.19361 1.689492 0 32.96009 2006 -0.19427 2.496323 0.565473 0.566632 1.364218 6.98E-07 0 -93.4763 2007 -0.16201 0.12335 0.203367 0.368705 -0.44872 0.339087 0 937.5683 2008 -0.11914 0.015954 0.208917 0.318636 -0.5116 0.401772 0 1118.764 2009 -0.09425 0.110462 0.179581 0.320557 -0.38968 1.213793 0 -89.4703 2010 0.006856 0.140663 0.171979 0.411355 -0.44267 1.170943 0 14.01166 2011 0.15077 0.147314 0.116713 0.387519 -0.35692 1.455637 0 16.93494
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
64
Cadbury Plc has return on asset ratio of 0.25 in the year 2002 and did not do well from 2006 -2009. The receivable ratio has 0.105 in 2001 and 2.50 in 2006. Their receivable ratio is low in the year 2007 to 2011; the stock turnover ratio is also low in the years under study. The company has more than 50% in 2000 but less than 50% in other years. Cadbury Plc has 0.931 as their highest payable in the year 2000 while they have less than 50% to pay in other years except in 2006 when their payable ratio is 0.566. CCC is very low during these years, and the company recorded 6.98 as liquidity ratio in 2006. In other years they did not make up to 2.0 as their liquidity ratio. This implies that they did not do well in those years, this company borrowed in2000 and 2001 only, but did not borrow in other years from 2002 – 2011. Their sales growth ratio is low in 2000, 2006, and 2009 they made the highest sales in 2008. Table 4.1.3: Raw Data for FlourMills Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.062955 0.084558 0.2701 0.383491 -0.56903 1.023931 0 -30.4156 2001 0.045528 0.105999 0.191431 0.354695 -0.44013 0.940391 0 30.2805 2002 0.121243 0.106399 0.171953 0.315678 -0.38123 0.986094 0.014902 40.04672 2003 0.079136 0.112289 0.22543 0.457299 -0.57044 0.667954 0.061983 -2.43955 2004 0.064044 0.116677 0.184553 0.410195 -0.47807 8.23164 0.077586 26.77674 2005 0.050743 0.09088 0.198615 0.373551 -0.48129 0.880119 0.091148 24.72301 2006 0.123591 0.040732 0.166926 0.332991 -0.45919 0.975365 0.094368 29.58734 2007 0.128599 0.042334 0.210144 0.101588 -0.2694 1.192708 0.043324 22.05919 2008 0.090501 0.042113 0.190231 0.077658 -0.22578 1.109464 0.130942 20.8133 2009 0.086655 0.029698 0.195372 0.063838 -0.22951 1 0.198082 41.05095 2010 0.170286 0.03076 0.195032 0.053804 -0.21808 1.00689 0.196633 14.73876 2011 0.10073 0.036113 0.234802 0.038454 -0.23714 1.327283 0.051569 15.57971
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
Flour mills did not do well in all the years under study. Their return on total asset ratio is low. None of the years has up to 0.20 they had little too receive and more to pay. Their liquidity ratio is low in almost all the years. Their highest liquidity ratio is 8.231 in 2014, other years do not have up to 2.0 the company did not borrow in 2000 and 2001 respectively. Their sales growth rate ratio is very high except in 2003 where their ratio is -2.439. Table 4.1.4: Raw Data for Nestle Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Rtio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.481512 0.036229 0.297164 0.041147 -0.30208 1.347511 0 -95.8007
2001 0.542715 0.038762 0.270756 0.061754 -0.29375 1.243079 0 41.07834
2002 0.538042 0.054848 0.243174 0.044004 -0.23233 1.32647 0 38.39675
2003 0.490925 0.02872 0.294709 0.121868 -0.38786 1.17994 0 25.80868
2004 0.455249 0.040198 0.211009 0.086666 -0.25748 1.072648 0 15.54538
2005 0.468611 0.033442 0.202017 0.065146 -0.23372 1.431038 0 20.64157
2006 0.433563 0.039647 0.240533 0.068358 -0.26924 1.57978 0 11.90268
2007 0.398252 0.052219 0.187945 0.086122 -0.22185 1.313177 0 14.58703
65
2008 0.406804 0.083199 0.204953 0.095891 -0.21764 1.382976 0.205094 17.52262
2009 0.311483 0.049805 0.267728 0.078163 -0.29609 0.99131 0.026949 32.03375
2010 0.302325 0.104984 0.193584 0.093108 -0.18171 1.026608 0.130988 17.25981
2011 0.240945 0.087637 0.173211 0.131958 -0.21753 0.937433 0.108809 22.28536
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
In Nestle Plc, the highest ratio of return on total assets is 0.542 in 2001, while the lowest is in 2011. This means that it was in 2001and 2002 that they made up to 50% profit. They had less to receive and more to pay too. Their stock turnover ratio is low while the CCC is too low. The highest liquidity ratio is 1.579 in 2006, with 0.937 in 2011. This company did not borrow from 2000 to 2007 their sales growth ratio is high except in 2000 when they had -95.801 as their ratio.
Table 4.1.5: Raw Data for Nigerian Bottling Company Plc. Years Return on
Asset Ratio Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.393536 0.813535 0.437983 0.263167 0.112385 1.120172 0.16795 -99.3784
2001 0.206563 0.005077 0.24116 0.262537 -0.49862 1.220904 0.016966 15400.98
2002 0.189522 0.068714 0.243749 0.340301 -0.51534 1.221606 0.00665 -82.223
2003 0.179394 0.000294 0.273788 0.289718 -0.56321 1.076829 0.001568 26064.26
2004 0.10425 0.021482 0.281922 0.262171 -0.52261 0.891911 0.0072 -98.9168
2005 0.081297 0.025977 0.284813 0.21873 -0.47757 0.740357 0.00155 16.59303
2006 0.041572 0.257943 0.24182 0.308382 -0.29226 0.668745 0.145827 -89.2371
2007 0.090393 0.022628 0.232747 0.261337 -0.47146 0.826391 0.17138 1048.382
2008 0.046941 0.033735 0.158216 0.024452 -0.14893 0.597816 0.127306 16.85642
2009 0.065202 0.044436 0.211645 0.274417 -0.44162 0.706016 0.140371 12.63138
2010 0.066978 0.045666 0.204737 0.267598 -0.42667 0.696161 0.925761 2.420694
2011 0.069759 0.050567 0.203583 0.25823 -0.41125 0.743172 1.345363 2.927982
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
This company has the highest profit of 0.394 in 2000 and the lowest of 0.0416 in 2006 they did not do well. They also had more to pay than to receive CCC is low while liquidity ratio is also low during these year. This company is all the year with highest debt ration of 1.345, and lowest of 0.001 in 2005, their sales growth ratio is low in 2000, 2002, 2004, 2006, their highest sales growth ratio is in 2003.
Table 4.1.6: Raw Data for Aluminum and Extrusion Company Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 -0.17457 0.029729 0.470203 0.920084 -1.36056 0.644753 0.132355 149.3861
2001 0.027075 0.012892 0.375608 0.590613 -0.95333 0.726668 0.129362 107.6118
2002 NA 0.030048 0.428695 0.72257 -1.12122 0.699932 0 -9.3444
2003 -0.10002 4.18E-08 0.39171 0.897196 -1.28891 0.54013 0.158807 0.055333
2004 -0.00359 0.020822 0.02797 0.294801 -0.30195 1.079555 0.574492 46.18196
2005 0.025307 0.017938 1.244713 1.63882 -2.86559 0.844166 0.489302 19.34508
2006 0.069129 5.012375 0.084038 0.309787 4.61855 0.330755 0 -99.8891
2007 0.123379 2.240985 0.112309 0.232824 1.895852 0.433575 0.089315 20.60725
66
2008 0.132689 0.003673 0.093191 0.374516 -0.46403 0.274149 0.066074 25.85389 2009 0.183129 0.00763 0.092482 0.298306 -0.38316 0.399638 0.022846 15.72687 2010 0.107863 0.00465 0.115071 0.03435 -0.14477 0.534355 0.030635 6.168729
2011 0.08267 0.002038 0.178404 0.042439 -0.2188 0.58354 0.043248 7.461922
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
This company did not make enough profit especially in 2002 where they made no profit. They have up to 5.012 as their receivable ratio in 2006 and lowest of 0.002 in 2011. Their stock turnover ratio is high in 2005 but low in other years. Liquidity ratio is 84% in 2005 and 27% in 2008. They did not borrow in 2002 and 2006. Their sales growth rate is high except in 2002 and 2006.
Table 4.1.7: Raw Data for BOC Cases Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.189275 0.129458 1.109496 0.985073 -1.96511 1.288734 0 -65.9259 2001 0.168305 0.113898 0.534775 0.576817 -0.99769 1.317834 0 23.40414 2002 0.22658 0.138085 0.546158 0.641237 -1.04931 1.326488 0 16.84696 2003 0.204921 0.172972 0.53871 0.65538 -1.02112 1.27238 0 5.95848 2004 0.105303 0.132226 0.47935 1.304705 -1.65183 0.645921 0.026881 11.35017 2005 0.070344 0.164616 0.344353 1.158746 -1.33848 0.699019 0.021972 12.39635 2006 0.114355 0.191811 0.314032 0.102248 -0.22447 2.160556 0 16.88493 2007 0.148103 0.181155 0.222247 0.081418 -0.12251 2.276143 0.021285 41.95916 2008 1.608196 0.180411 0.216783 0.509058 -0.54543 2.340526 0 7.075497 2009 2.137864 1.921697 0.204774 4.716995 -3.00007 2.314747 0 -88.9498 2010 0.244451 0.154894 0.298365 0.070862 -0.21433 1.453875 0 944.6215 2011 0.230765 0.227649 0.26413 0.080281 -0.11676 1.723196 0 2.030441
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
BOC Cases Plc did well in 2009 because it made more profit and in other years it did not do well. The highest receivable ratio is 1.921 while the highest payable ratio is 0.985 and lowest of 0.071, CCC was low, liquidity ratio was encourage in 2006, 2007,2008 and 2009 respectively their lowest liquidity ratio is 0.645, they borrowed only in 2004, 2005 and 2007, but did not borrow in other years. Table 4.1.8: Raw Data for First Aluminum Plc. Years Return
on Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.034205 0.132706 0.458108 0.554159 -0.87956 1.138943 0.017556 37.45874
2001 -0.05427 0.166345 0.302798 0.574776 -0.71123 0.857254 0.097599 21.64458
2002 -0.07504 0.219035 0.322809 0.781648 -0.88542 0.743331 0.037068 3.929083
2003 0.060228 0.224367 0.31564 0.595556 -0.68683 0.986241 0 17.73386
2004 0.029809 0.14952 0.252935 0.459725 -0.56314 0.943693 0.067587 32.34261
2005 0.039676 0.141898 0.243504 0.427539 -0.52915 0.968488 0.038525 26.70396
2006 0.00423 1.475335 0.4414 0.66276 0.371175 0.931358 0.035696 -89.3193
67
2007 0.013302 0.152696 0.449109 0.76377 -1.06018 1.144051 0.032279 906.843
2008 0.054515 0.110184 0.47579 0.804198 -1.1698 0.995711 0.023784 -7.2634
2009 0.005564 0.110547 0.412092 0.507059 -0.8086 1.052736 0.02233 2.598037
2010 -0.02837 0.068609 0.372038 0.141473 -0.4449 1.025672 0 5.675414
2011 -0.02823 0.049816 0.340095 0.122823 -0.4131 1.023692 0 0.33763
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
This company did not make enough profit. The highest return on asset ratio is 0.034 in 2000. The receivable ratio is low while that of payable is higher. CCC is negative and liquidity ratio is better in 2000, 2007, 2009 2010 and 2011 respectively. The company did not borrow in 2003, 2010 and 2011. Generally, their sales
growth ratio is high. They made huge sales still they could not make enough profit.
Table 4.1.9: Raw Data for Nigeria Enamelware Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.055609 0.01741 0.20302 0.228862 -0.41447 1.067457 0 -85.3937
2001 0.046812 0.019345 0.256754 0.277148 -0.51456 1.07913 0 29.50315
2002 0.044657 0.06316 0.238739 0.295182 -0.47076 1.106424 0 0.647805
2003 0.035341 0.11977 0.231017 0.490015 -0.60126 0.875633 0 6.281947
2004 0.027997 2.796533 0.275529 0.450197 2.070807 1.455332 0 -90.7981
2005 0.040731 0.202927 0.2981 0.446521 -0.54169 1.196748 0 985.5856
2006 0.037447 0.09133 0.257648 0.4743 -0.64062 12.3837 0 -11.4427
2007 0.031938 0.055467 0.253461 0.686061 -0.88405 1.222207 0 -0.28251
2008 0.032037 0.016147 0.231522 0.759021 -0.9744 1.223348 0 -3.75639
2009 0.091262 0.114505 0.132662 0.395962 -0.41412 1.164838 0 59.79402
2010 0.087434 0.013886 0.167634 0.01001 -0.16376 1.205792 0 -2.3203
2011 0.121361 0.021459 0.264007 0.027001 -0.26955 1.309605 0 0.345576
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
The return of asset ratio of this company is low, None of the companies got up to 20% of profit. They have more to receive then to pay. This company has liquidity ratio of more than 1.0 in all the years except in 2003 where it has 0.875 which is too low, they did not borrow at all in the years under study. The highest sales growth ratio is 985.58 in 2005 and low ratios in other years.
Table 4.1.10: Raw Data for VitaFoam Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 4.91608 0.019497 0.238571 0.412514 -0.63159 1.259264 3.169866 -2.55417
2001 0.790495 0.029334 0.169614 0.325117 -0.4654 1.272154 0.428224 45.97292
2002 0.705918 0.701703 1.672614 3.511045 -4.48196 1.267943 0.493476 -88.9543
2003 0.696921 0.069525 0.196427 0.55307 -0.67997 1.301187 0.499186 946.024
2004 0.267607 0.070609 0.229983 0.358249 -0.51762 1.588526 0.2291 -6.07238
68
2005 0.089483 0.072786 0.332352 0.307538 -0.5671 1.628719 0.170737 -3.4377
2006 0.125305 0.071339 0.302631 0.392664 -0.62396 1.486494 0.095042 15.18871
2007 0.172302 0.044743 0.522885 0.567299 -1.04544 1.606318 0.088006 51.43039
2008 0.089192 0.045076 0.600511 0.604295 -1.15973 1.537045 0.136418 26.35814
2009 0.101853 0.048116 8.0196 6.538124 -14.5096 1.613712 0.070708 1.79639
2010 0.134751 0.071099 0.292533 0.10704 -0.32847 1.36147 0.003727 34.31674
2011 0.140849 0.063647 0.472434 0.487577 -0.89636 1.312799 0.006186 21.39204
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
Vita foam Plc made enough profit of 4.914 in 2000 but little in other years. It also had more o pay than more to receive. Their CCC is nothing to write home about. If approximated their liquidity ratio is up to 2 in 2004, 2005, 2006 2007 2008 and 2009. This implies that in these years they can be able to settle their financial obligations. They borrowed in all the years under study. The sale growth ratio is high from 2006 to 2011 and also in 2001.
Table 4.1.11: Raw Data for Vono Products Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.049152 0.320705 0.313411 0.68763 -0.68034 1.39601 0 -97.5338
2001 0.009833 0.364824 0.50508 0.914108 -1.05436 1.188563 0 -1.62607
2002 0.056677 0.185077 0.519558 0.708302 -1.04278 1.178557 0 21.44706 2003 0.06283 0.151616 0.588987 0.636499 -1.07387 1.296213 0 15.80251 2004 -0.80435 0.402993 0.284609 0.692139 -0.57375 0.55205 0 -35.5932 2005 -0.21107 0.079839 0.479424 1.074428 -1.47401 1.446484 0 -6.63423 2006 0.035496 0.092677 0.782238 3.427611 -4.11717 0.56206 0 14.03441 2007 -0.48964 0.108948 0.093453 0.554162 -0.53867 0.405234 0 365.3164 2008 -0.12629 0.097411 0.248673 1.116453 -1.26771 0.390741 0 -55.1426 2009 -0.12209 0.247299 0.167828 2.390098 -2.31063 0.273435 0 -28.894 2010 -0.18286 0.206023 0.215318 2.984274 -2.99357 0.390633 0.192664 -2.34065 2011 -0.13616 0.222578 2.289354 2.493004 -4.55978 0.370545 0 23.37397 Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
This company did not make profit in 2004, 2006,2007 and2008 but made little profit in other years. The company had more to pay than to receive. Stock turnover ratio is low and liquidity ratio is better in the first four years and also in 2005, there was no borrowing in the year 2000 -2011 sales growth ratio is higher in 2007 followed by 2011, while this sales ratio in many year are negative.
69
Table 4.1.12: Raw Data for Evans Medical Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.034625 0.200615 0.500073 0.360476 -0.65993 0.650436 0 39.05726
2001 0.029339 0.121096 0.5048 0.303518 -0.68722 0.753833 0 22.50026
2002 0.065951 0.141989 0.578437 0.310066 -0.74651 0.965887 0 28.72962 2003 0.054482 0.225431 0.678359 0.318362 -0.77129 1.483619 0 29.93023 2004 -0.01729 0.194029 0.567678 0.177332 -0.55098 1.134561 0 54.04279 2005 0.028394 0.208988 0.806809 0.209004 -0.80682 1.237001 0 6.804 2006 0.04886 0.231519 0.832432 0.228932 -0.82985 1.193643 0 14.98197 2007 -0.08589 0.207648 1.041209 0.331742 -1.1653 0.973779 0 8.364676 2008 -0.08256 0.225626 0.724474 0.397054 -0.8959 0.842645 0 41.67471 2009 -0.24173 0.180554 0.665725 0.494238 -0.97941 0.609483 0 -21.0859 2010 0.031798 0.219795 0.537572 0.550305 -0.86808 0.99945 0 29.75194
2011 0.010873 0.348139 0.449646 0.534846 -0.63635 1.018916 0 -13.766 Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
Evans Plc did not make profit from 2007 – 2009, and little profit in other years. They have more to pay then to receive, while liquidity ratio is more than 1.0 from 2003-2006 and in 2011. The other years are not up to 1.0. Evans Plc did not borrow in all the years. They made huge sales in all the years except in 2009 and 2001. Table 4.1.13: Raw Data for May and Baker Nigeria Plc. Years Return on
Asset Ratio Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.066064 0.216198 0.563288 0.492636 -0.83973 2.015839 0 -76.212
2001 0.150865 0.30818 0.592333 0.709101 -0.99325 1.794236 0 12.51061
2002 0.070925 0.246188 0.493328 0.47496 -0.7221 2.073639 0 20.81539
2003 0.105454 0.191962 0.42294 0.452129 -0.68311 1.780608 0 39.65755
2004 0.094409 0.173053 0.429675 0.066085 -0.32271 2.217934 0.055875 6.763298
2005 0.07945 0.141987 0.427996 0.061357 -0.34737 2.33457 0.253096 5.056067
2006 0.067142 0.110614 0.538486 0.110545 -0.53842 2.330899 0.010669 12.84018
2007 0.103268 0.190242 0.324683 0.145158 -0.2796 1.691455 0 71.2864
2008 0.123612 0.102321 0.257904 0.105433 -0.26102 1.533621 0.06543 40.93948
2009 0.055926 0.114615 0.33705 0.089207 -0.31164 1.117355 0.069466 -15.3578
2010 0.045151 0.176017 0.454535 0.121577 -0.40009 0.987145 0.124106 0.754573
2011 0.048182 0.134381 0.314421 0.115176 -0.29522 0.713063 0.106008 4.275843
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
This company did not make enough during the year 2000 – 2011. Their receivable ratios are low while their payable ratios are also low, but they have more to pay than to receive. The lowest liquidity ratio is 1.117 while others are more. This shows that the company is liquid enough to settle its obligations. They did not borrow from 2000 to 2003 and 2007. The sales growth rate is high except in 2000 and 2011.
70
Table 4.1.14: Raw Data for Pharma-Deko Nigeria Plc. Years Return
on Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 -0.28955 0.354035 0.641718 2.368614 -2.6563 0.489257 0 -97.9008
2001 -0.0135 0.224999 0.439907 1.444802 -1.65971 0.568342 0 125.8291
2002 0.145708 0.23696 0.200375 0.962236 -0.92565 0.82401 0 78.30239
2003 0.12896 0.242828 0.18933 0.803619 -0.75012 1.222433 0 49.24213
2004 0.04528 0.355082 0.343871 1.147123 -1.13591 0.933075 0 16.74983
2005 0.096513 0.022456 1.719287 2.230389 -3.92722 1.015897 0 691.7326
2006 -0.24871 0.169461 0.165785 1.730685 -1.72701 0.366942 0 -88.4972
2007 -0.16012 0.226577 0.068243 2.452934 -2.2946 0.346626 0 21.81199
2008 -0.13097 27.87963 0.087971 1.865503 25.92615 0.347666 0 -85.3775
2009 0.199647 0.105479 3.121161 3.908496 -6.92418 0.204843 0 334.2857
2010 -0.2259 0.194207 0.408095 0.746414 -0.9603 0.509943 0.341589 -1.48885
2011 0.024817 0.042818 0.367094 0.453319 -0.7776 0.569618 0.361385 155.2044
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
Return on asset ratio of this company is low while they had more to pay than to receive. CCC is negative. Liquidity ratio is low. They have up to 1.0 in only 2003 and 2005. It is in only 2010 and 2011 that this company borrowed they did not borrow in other years. Sales growth ratio is high except in 2000, 2006, 2008 and 2010.
Table 4.1.15: Raw Data for Benue Cement Company Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 -0.10239 0.257883 2.192216 3.416137 -5.35047 0.434935 0.049438 -37.2788
2001 -0.27937 0.309324 1,.230452 2.4361 -3.35723 0.426871 0.324863 40.8999
2002 -0.48596 0.417114 1.730596 5.679208 -6.99269 0.200208 0.150877 -47.7203
2003 -0.47319 0.614822 0.158396 3.175943 -2.71952 0.065602 0.011947 -32.9348
2004 -0.12134 0 0 0 0 0 0 0
2005 -0.06948 0.243327 0.542425 2.003753 -2.30285 0.09676 0.039317 0
2006 0.061145 0.277999 0.120833 1.405418 -1.24825 0.26009 0.192292 50.53825
2007 0.050877 0.30078 0.14593 1.417717 -1.26287 0.124063 0.009276 -9.21796
2008 -0.49101 0.35029 0.122604 1.224063 -0.99638 0.198604 0.078497 -21.0928
2009 -0.09077 0.616123 2.725822 15.7122 -17.8219 0.147949 0.023004 46.62865
2010 -0.08465 0.618571 18.98004 12.11783 -30.4793 0.219682 0.015857 0.290235
2011 -0.1001 0.573751 17.44545 13.06192 -29.9336 0.183523 0.022139 5.599682
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
71
This company did not make profit in almost all the years, it was in 2006 and 2007 that they made little profit. They had more to receive than to pay. Liquidity ratio is low, and they borrowed in almost all the years.
Table 4.1.16: Raw Data for Berger Paints Nigeria Plc. Years Return on
Asset Ratio Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.040842 0.225659 0.624668 0.711688 -1.1107 1.014841 0 -83.2194
2001 0.149082 0.015042 0.47907 0.520509 -0.98454 1.477945 0 1271.29
2002 0.104144 0.029615 0.623666 0.887832 -1.48188 3.70502 0 -5.96092
2003 0.027384 0.304388 0.344477 0.326061 -0.36615 2.768688 0 -89.8095
2004 0.101776 0.185786 0.374117 0.199954 -0.38828 1.070067 0 24.72222
2005 -0.03298 0.163377 0.345622 0.167599 -0.34984 0.780299 0 3.774959
2006 0.055236 0.127628 0.265374 0.145622 -0.28337 0.876194 0 20.1845
2007 0.105111 0.113769 0.316593 0.142209 -0.34503 1.074013 0 -1.09792
2008 0.119973 0.067981 0.214389 0.107955 -0.25436 1.495632 0 11.39888
2009 0.141529 0.086668 0.22027 0.117995 -0.2516 1.663335 0 -6.1101
2010 0.199542 0.075142 0.354545 0.098011 -0.37741 1.966739 0 15.83131
2011 0.09189 0.041688 0.35829 0.15541 -0.47201 2.004241 0 -6.61135
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
The return on asset ratio of this company is low. Receivable ratio is low and their payable ratio is also low. The company has liquidity ratio of 3.705 in 2002 which is comfortable. Their lowest liquidity ratio is 0.780 they did better than majority of these companies under study. They did not borrow in all the years. They made the highest sales in 2001. Table 4.1.17: Raw Data for Premier Paints Nigeria Plc. Years Return on
Asset Ratio Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.031775 0.220372 0.090847 0.235076 -0.10555 1.359711 0 4563.757
2001 -0.01297 0.188504 0.126416 0.244712 -0.18262 0.739277 0 23.95242
2002 -0.04412 0.269069 0.140902 0.347373 -0.21921 1.449073 0 14.48656
2003 -0.07276 0.260572 0.144533 0.371088 -0.25505 0.985215 0.019877 16.9352
2004 -0.03865 0.139605 0.094559 0.216466 -0.17142 0.772511 0 -6.88844
2005 0.033165 0.15418 0.134137 0.245011 -0.22497 1.056572 0.111846 1.911046
2006 0.059564 0.075673 0.215312 0.321044 -0.46068 0.828867 0 7.420559
2007 0.042811 0.119833 0.203366 0.043095 -0.12663 0.66117 0 -8.40279
2008 0.042385 0.114605 0.219709 0.36006 -0.46516 1.179122 0.187566 26.29176
2009 -0.07876 0.138092 0.205996 0.566807 -0.63471 2.726085 0 -99.9049
2010 -0.31878 0.089328 0.075798 0.798313 -0.78478 0.235933 0.104972 -25.703
2011 -0.33598 0.121703 0.128706 0.918849 -0.92585 0.321282 0.457924 10.04324
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
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This company did not make profit in many years, it was only in 2005 -2008 that they made little profit. They also have more to pay than to receive. The stock ratio is also low, while liquidity ratio is low, but manageable in 2000, 2003, 2005, 2008 and 2008. They borrowed in only 2003, 2005, 2008, 2010 and 2011. They have the highest sales in 2000 followed by 2008.
Table 4.1.18: Raw Data for Guinness Nigeria Plc. Years Return on
Asset Ratio Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 2.247028 0.063444 0.46237 0.819921 -1.21885 1.685756 0 8008.361
2001 2.373897 0.044891 0.423432 0.886579 -1.26512 1.635157 0 34.14634
2002 2.475667 0.068125 0.050217 0.809349 -0.79144 1.530557 0 48.61583
2003 2.782544 0.044512 0.402423 0.717165 -1.07508 1.275824 0 28.98812
2004 3.225994 0.074574 0.599913 0.863647 -1.38899 1.279394 0 24.68406
2005 2.295494 0.030967 0.575155 0.577808 -1.122 1.354934 0 -1.36635
2006 2.175907 0.060227 0.464473 0.649673 -1.05392 1.457979 0 14.49534
2007 2.26966 0.106997 3.725657 4.611446 -8.23011 1.558861 0 16.0547
2008 1.985516 0.120222 0.361333 0.481756 -0.72287 1.419631 0 11.09354
2009 1.874018 0.102132 0.362241 0.386106 -0.64622 1.293253 0 28.87745
2010 0.242115 0.121209 0.261913 0.383124 -0.52383 1.220605 0.01573 22.67995
2011 0.283829 0.14664 0.254067 0.383899 -0.49132 1.214107 0.014344 13.07172
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
Guiness Plc made enough profit in all the years except in 2010 and 2011. They really preformed well. They did not have too much to receive. The stock ratio is not too high or too low. The liquidity ratio is better than most of these companies. They borrowed in only 2010 and 2011, but did not borrow in other years. They
also made huge sales except in 2005. Table 4.1.19: Raw Data for Nigeria Breweries Plc. Years Return on
Asset Ratio Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio Sales Growth Rate (%)
2000 0.187997 0.062143 0.565708 1.330301 -1.83387 0.708631 0 -79.1743 2001 0.20132 0.083651 0.596933 1.47014 -1.98342 0.862055 0 49.25301 2002 0.157326 0.131633 7.780552 2.274686 -9.9236 0.729929 0.000232 11.49092
2003 0.12917 0.048905 0.053668 2.048696 -2.05346 0.676646 0 32.02814
2004 0.135789 0.038671 0.55443 1.606228 -2.12199 0.725712 0 34.72451
2005 0.178149 0.022242 0.030644 0.517608 -0.52601 0.71536 0 5.119847
2006 0.217247 0.055284 0.301781 0.389265 -0.63576 1.036894 0 7.726235
2007 0.307862 0.067882 0.307374 0.079327 -0.31882 1.621691 0 29.45506
2008 0.342777 0.037818 0.265875 0.270764 -0.49882 1.193719 0 17.61987
2009 0.386958 0.021859 0.253101 0.278626 -0.50987 0.889194 0 24.93084
2010 0.392346 0.034679 0.215119 0.25832 -0.43876 0.8976 0 13.18821
2011 5.305932 0.351184 0.237804 0.261137 -0.14776 0.927166 0.033012 -89.4971
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
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This company made the highest profit in 2010 with return on asset ratio of 0.392, it also has little to receive but more to pay. The liquidity ratio is low. Only 2006, 2007 and 2008 have more that 1.0, they borrowed in only 2002 and 2011, but the ratio is low. The sales growth rate is high except in 2006, and 2011 where they have negative ratio. Table 4.1.20: Raw Data for AVON Nigeria Plc. Years Return
on Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.054965 0.555617 0.011554 0.508627 0.035435 1.31452 0.055777 -88.4762 2001 0.032087 0.611437 0.013375 0.407161 0.1909 1.784638 0.008413 5.584376 2000 0.054965 0.555617 0.011554 0.508627 0.035435 1.31452 0.055777 -5.28902 2001 0.032087 0.611437 0.013375 0.407161 0.1909 1.784638 0.008413 5.584376 2002 0.034374 0.659469 0.013494 0.563668 0.082306 1.400889 0.462862 27.76296 2003 0.090574 0.70063 0.012133 0.70371 -0.01521 1.160882 1.085291 17.55113 2004 0.223693 0.299225 0.258162 0.520169 -0.47911 0.617504 0.037123 -95.1786 2005 0.572117 1.137001 0.298611 0.360075 0.478315 3.365619 0.034906 -32.7802 2006 0.057896 0.435837 0.247423 0.445365 -0.25695 1.147865 0.001356 5224.027 2007 0.066702 0.443952 0.291999 0.441639 -0.28969 1.212119 0.006041 0.373878 2008 0.059838 0.71869 0.454237 0.572514 -0.30806 11.33401 0.043699 -5.78091 2009 0.054331 0.753927 0.445897 0.611554 -0.30352 1.211586 0.009772 34.5 2010 0.019567 0.138631 0.565879 0.029333 -0.45658 1.193709 0.033012 42.184 2011 0.024522 0.142663 0.386691 0.033419 -0.27745 1.264542 0.014386 5.670915 Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
This company has low profit, low receivable, low payable, but higher liquidity ratio. CCC is higher from 2000-2002, they borrowed in all the years under study but the ratio is low. Their sales growth rate is not too encouraging especially 2000, 2002, 2005 and 2008. Table 4.1.21: Raw Data for BETA GLASS Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 0.34548 0.083305 0.56962 1.227466 -1.71378 0.802416 0 -88.9941
2001 0.078735 0.068712 0.604436 1.467064 -2.00279 0.734279 0 37.17858
2000 0.34548 0.083305 0.56962 1.227466 -1.71378 0.802416 0 -27.1023
2001 0.078735 0.068712 0.604436 1.467064 -2.00279 0.734279 0 37.17858
2002 -0.20646 0.068338 0.682502 1.413858 -2.02802 0.78736 0 7.764795
2003 -0.02561 0.096698 0.542303 0.947552 -1.39316 0.59046 0 72.72593
2004 0.157362 0.170062 0.400486 0.848305 -1.07873 0.838901 0.024207 167196.2
2005 0.060098 0.166906 0.511382 0.927035 -1.27151 0.833077 0.011074 6.973513
2006 -0.00129 0.11657 0.533668 1.128768 -1.54586 0.63737 0 7.744265
74
Continued from table 4.1.21
Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
Beta Glass has the highest return on asset of 0.345. Infact, the company did not do well at all. Their payable ratio is high and their receivable ratio is low. The CCC ratio is negative. They have highest liquidity ratio of 2.399 in 2011 and lowest of 0.106 in 2009. They did better from 2008 – 2011 than in other years. They only borrowed from 2004 to 2009. They did not borrow in other years. They also made huge sales, but did not make any sale in 2009. Table 4.1.22: Raw Data for INCAR Nigeria Plc. Years Return on
Asset Ratio
Accounts Receivable Ratio
Stock Turnover Ratio
Accounts Payable Ratio
Cash Conversion Cycle Ratio
Liquidity Ratio
Debt Ratio
Sales Growth Rate (%)
2000 -0.02023 0.211613 0.316651 0.26557 -0.37061 2.2511 0 -99.2106
2001 0.020077 0.166968 0.390773 0.255363 -0.47917 2.497668 0 -3.05902
2002 -0.17036 0.245182 1.273556 0.787858 -1.81623 1.285351 0 -51.1953 2003 -0.1598 0.107984 0.271873 0.318117 -0.48201 1.453845 0 251.1909 2004 0.573668 0.438517 0.300339 0.296444 -0.15827 0.579928 0 3.041686 2005 0.075197 0.717616 0.298611 1.706349 -1.28734 0.710216 0.167333 -32.7802 2006 0.02504 3.710533 0.45748 0.410707 2.842346 4.71814 0.155922 -5.70097 2007 -0.01791 5.182049 0.499541 0.603812 4.078696 16.20123 0 17.00406 2008 0.012452 0.639761 0.233821 0.427836 -0.0219 2.854802 0 133.2685 2009 0.140629 0.15798 0.355916 0.93647 -1.13441 4.322751 0 290.9986 2010 0.303165 0.146475 0.359142 0.857778 -1.07045 4.828046 0 10.71456 2011 0.346697 0.129538 0.215988 0.5631 -0.64955 0.523721 0 16.27126 Source: Author’s Computation from Annual Accounts of Firm 2000-2011.
It is true that this company did not make enough profit, but it is the most liquid of all the companies under study. It has more to receive than to pay. They borrowed in only 2005 and 2006, but did not borrow in other years. They made huge sales especially in 2003, 2007 – 2011.
2007 0.018956 0.096404 0.523652 0.865036 -1.29228 0.836468 0.001097 26.1771
2008 0.191068 0.060386 0.424581 0.437903 -0.8021 1.13941 0.072007 47.56765
2009 0.236358 0.084422 0.374341 0.514075 -0.80399 1.106158 0.051679 0
2010 0.113309 0.180154 0.286072 0.079622 -0.18554 2.168452 0 -99.9059
2011 0.114813 0.134937 0.248194 0.118584 -0.23184 2.399884 0 13.95163
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4.2 Descriptive Statistics The descriptive statistics is organized along manufacturing sector pattern. The choice of selected industries (manufacturing) is influenced by data availability. The essence is to minimize the influence of missing observations on our results and also capture the dominant manufacturing industries in Nigeria (see Appendix 1 for the list of selected industries). In this section, descriptive statistics is organized along a cross section of industries in the Nigerian manufacturing environment. The seven (7) sub- sectors of the manufacturing industries include, food and beverages ,Industrial/ domestic products, Healthcare, Building materials and chemicals, breweries, packaging, and automobile and tyre. Table 4.2.1: Descriptive statistics showing minimum, maximum, mean, standard deviation and variance values of Pooled variables for all Twenty two firms considered in the study
N Minimum Maximum Mean Std. Deviation Variance
PROFIT 262 -.4896 509.0000 2.166550 31.4391637 988.421
ACCOUNTS
RECEIVVABLE
.0000 1804.0000 7.219990 111.4448748 12419.960
INVENTORY 262 .0000 18.9800 .588034 1.7352739 3.011
ACCOUTS PAYABLE 262 .0000 15.7122 .775915 1.6027982 2.569
CASH CONVERSION
CYCLE
262 -30.4793 7.4138 -.262435 2.8109878 7.902
LIQUIDITY 262 .0412 16.2012 1.279420 1.4808763 2.193
DEBT 262 .0000 3.1698 .076842 .2508745 .063
SALES GROWTH 262 -607.0000 167196.1000 805.352024 1.0450740E4 1.092E8
Valid N (list wise) 262
Source: computed from Handpicked Data from the Annual Reports and Accounts of quoted
manufacturing companies, 2000-2011, and the Factbook 2010/2011, for firms in the Twenty two
firms considered in the study
The results shown in the table indicates that the twenty two manufacturing firms considered in this study
have liquidity rate of 1.28%. This shows that their liquidity ratio generally is poor within the period under
study. It is not up to 2 which is more comfortable when current ratio is used instead of acid test ratio. The
sales growth rate is high above 100% which shows that they should be careful to avoid bad debt.What they
would receive is also above average mean of 7.2199. They are making a lot of profit even when their
liquidity ratio is not too good. Their stock average rate is not too small or too low with 59% while their
payable is up to 78%, which will affect their profit. CCC is very low, which means that generally these
companies are not managing their working capital up to expectation. Their debt ratio is low showing that
they did not borrow much externally. In the study of Arunkumar and Ramanan (2013), the Indian industry
had a high liquidity with average ratio of 3.76, approximately 4; which disagrees with the average ratio of
this study. Sales also had an average of 1.3 times which in not in line with sales growth rate of the
76
companies under study. Again the manufacturing firms in Pakistan had an average of 14% on profitability
which is consistent with the profitability ratio and sales growth rate of this study.
4.2.2 Food and beverages Sub-sector
For this company, the selected firms based on the availability of data include; seven-up bottling company plc, Cadbury Nigeria plc, flour mills Nigeria plc, and Nigeria bottling company. Table 4.2.2 presents the descriptive statistics of the five (5) selected companies from this sub-sector.
Table 4.2.2: Descriptive statistics showing minimum, maximum, mean, standard
deviation and variance values of variables for the Food and Beverages Sector
N Minimum Maximum Mean Std. Deviation Variance
PROFIT 60 -.1942 .5427 .154678 .1617260 .026
ACCOUNTS RECEIVABLE
60 .0029 1.0597 .120215 .1679034 .028
INVENTORY 60 .0000 .5698 .242943 .0980667 .010
ACCOUNTS PAYABLE
60 .0000 .9308 .265350 .2003037 .040
CASH CONVERSION
60 -.5442 .8965 .017606 .2589330 .067
LIQUIDITY 60 .3390 1.8619 1.100456 .3163283 .100
DEBT 60 .0000 1.3454 .114145 .2418464 .058
SALES GROWTH 60 -98.9200 26064.2600 760.048935 3.8835282E3 1.508E7
Valid N (listwise) 60
Source: computed from Handpicked Data from the Annual Reports and Accounts of 5 quoted companies,
2000 - 2011, and the Factbook, 2010/2011 for firms in the Food and Beverages (SPSS
computation, version 17.1 analytical software)
We can see from table 4.2.2 that the rate of receivables under food and beverages companies is 12%. This
means that they are managing their debtors well since only few people are still owing them. Then
coming to payables, they are able to have outstanding of 26% unpaid. Their payables are more
than what they would receive. They should watch it. Liquidity ratio is 1.10, which means that they
are slightly liquid enough to meet up with their obligations, but if it is up to 2 it would have been
better. The stock turnover in Food and Beverages is 24% (mean=.2429), this also implies that they
are mindful of their stocks which they keep. If they keep a lot of stocks they may become obsolete
or affect the liquidity of the companies, sales growth rate is very high with over 100%, this also
shows that they are making a lot of sales, but they should be careful because this could amount to
bad debt and stock-outs which may cause problem and affect liquidity. Coming to CCC, we can
77
see that the rate cash flows from suppliers to inventories, to receivables and back into cash is very
low (mean= 0.17) this is very poor and it may hinder the company from having enough cash to
pay its obligations.
4.2.3: Industrial and Domestic Products Sub-Sector
Based on the availability of data, the study selects six (6) companies under this sub-sector; Aluminum
Extrusion Industries PLC, B.OC. cases plc. First Aluminum Nigeria PLC, Nigeria Enamelware PLC, Vita
foam Nigeria plc and Vono Products plc.
Table 4.2.3 Descriptive statistics showing minimum, maximum, mean, standard deviation and
variance values of variables for the Industrial and Domestic Products Sector
Variables N Minimum Maximum Mean Std. Deviation Variance
N Minimum Maximum Mean Std. Deviation
Profit 72 -.4896 4.9160 .190907 .6648831 Profit
ACCOUNTS RECEIVABLE
72 .0020 1804.0000 25.295624
212.5753145 AR
INVENTORY 72 .0279 8.0195 .477768 .9596433 INVENTORY
ACCOUTS PAYABLE
72 .0100 4.7169 .650795 .7963556 AP
CASH CONVERSION CYCLE
72 -4.5598 7.4138 -.009194 1.2913363 CCC
LIQUIDITY 72 .2734 12.3836 1.245759 1.4131217 LIQUIDITY
DEBT 72 .0000 3.1698 .109062 .3869650 DT
SALES GROWTH
72 -607.0000 985.5000 52.859763
236.2377560 SL
Source: computed from Handpicked Data from the Annual Reports and Accounts of 6 quoted companies,
2000 - 2011, and the Factbook, 2010/2011 for firms in the Industrial and Domestic
Products Sector
The mean of profitability ratio is about 19% and the mean of sales growth is 53%. The result indicate that
in the average that every N53 sales/turnover of the selected companies N19.0k was earned as profit. The
receivables in these companies have mean of 25.30(25%). This means that they were making a lot of sales
without minding whether the payment is made to them or not. They should pray that some of the receivables
would not become bad debt. Their liquidity is not too good and also not too bad. But the rate of the CCC is
78
also poor while the rate of their payable was low. Therefore they are not owing so much debt. It shows in the
debt average ratio of .11.
4.2.4: Healthcare Sub-Sector
Based on the availability of data, this study selects the following firms from Healthcare companies: Evans
medical plc, May and Baker Nigeria plc and Pharma-Deko plc.
Table 4.2.4 is the descriptive statistics of selected Healthcare Sub- sector under manufacturing firms. Table 4.2. 4: Descriptive statistics showing minimum, maximum, mean, standard
deviation and variance values of variables for the Health Sector
N Minimum Maximum Mean Std. Deviation Variance
Profit 36 -.2896 .2000 .013601 .1214106 .015
ACCOUNTS RECEIVABLE
36 .0207 27.8800 .957749 4.6159511 21.307
INVENTORY 36 .0682 3.1212 .577688 .5257076 .276
ACCOUNTS PAYABLE
36 .0614 3.9085 .757665 .8675330 .753
CASH CONVERSION CYCLE
36 -2.1581 1.0095 -.023640 .7367141 .543
LIQUIDITY 36 .2048 2.3346 1.106720 .5961645 .355
DEBT 36 .0000 .3614 .038547 .0920927 .008
SALES GROWTH
36 -85.3800 691.7300 49.422831
128.4024824 16487.197
Valid N (listwise)
36
Source: computed from Handpicked Data from the Annual Reports and Accounts of 3 quoted companies, 2000 - 2011, and the Factbook, 2010/2011 for firms in the Healthcare Sector
Results based on the descriptive statistics analysis show that the profitability of firms in this sub-sector is not
effective. Considering the accounting measure, it is found that the average return on Assets (ROA) is not up
to 1% (mean =0.0136) for the period under consideration. This shows that the managers in this sub-sector do
not manage their assets by conveying them into liquid cash. This is because they invested more on assets
and they earn less.
When we focus on the receivables, there is an average of 96% (mean=.9577). This shows that the rate of this
sub-sectors receivable is high. They should also watch out to avoid bad debt. Their payables are less than the
receivables while stock turnover shows an average of 58% (mean= 0.5775) above average. Their liquidity
79
ratio is slightly alright but they should put effort to bring the average rate to 2, so that they can be able to
settle their obligations to a reasonable extent. The results also show that as sales rate grows, so their
receivables grow, but their CCC rate was poor. The Healthcare sub-sector were not managing the flow of
cash to inventory, to receivable and back to cash well. They should put more effort to do better in
subsequent years. Their external long term debt was not much.
4.2.5 Building Materials and Chemical Sub-Sector
Based on availability of data, three (3) firms were selected from this sub-sector. The names of the selected
firms include: Benue cement company PLC, Berger paints Nigeria PLC and premier paints PLC. Table 4.2.5
presents the descriptive statistics of companies under Building materials/chemicals.
Table 4.2.5: Descriptive statistics showing minimum, maximum, mean, Profit-high
standard deviation and variance values of variables for the Building Materials and
Chemical Sector
N Minimum Maximum Mean Std.
Deviation Variance
Profit 36 -.4860 509.0000 14.124506 84.8360023 7197.147
ACCOUNTS RECEIVABLE
36 .0000 .7005 .231913 .1882319 .035
INVENTORY 36 .0000 18.9800 1.454493 4.1693273 17.383
ACCOUNTS PAYABLE
36 .0000 15.7122 1.911959 3.7948111 14.401
CASH CONVERSION CYCLE
36 -30.4793 .7446 -1.986858 6.9884335 48.838
LIQUIDITY 36 .0412 3.7050 .880863 .8645677 .747
DEBT 36 .0000 .4579 .047964 .1009669 .010
SALES GROWTH
36 -99.9000 1271.3000 41.399170 213.5174144 45589.686
Valid N (listwise)
36
Source: computed from Handpicked Data from the Annual Reports and Accounts of 3 quoted
companies, 2000 - 2011, and the Factbook, 2010/2011 for firms in the Building Materials
and Chemical Sector
Table 4.2.5 results show that the average receivables of firms in this sub-sector is 23% which is not up to
50%, but the problem is that they have huge rate of their payables above 100% mean =(1.9119). This means
80
that even when their sales growth was low, they had much debtors, which goes on to mean that they are not
collecting more cash and their debtors do not pay. This is not encouraging.Another problem in building
materials sub-sector is that their liquidity ratio is low (mean=0.88) not up to 1, let alone 2 which is the
standard rate for enough liquidity under current ratio . Their stock turnover was more than 100% which is
not good. What they owe to the outsiders is not significant with mean of 0.0479. In spite of all these, the
profitability ratio was high with the mean of 14.1245. This shows that the financial performance (the result
of operations profitability) of firms in this sub-sector is effective. Therefore despite their low rate of
liquidity they still made enough profit during the years under study. Therefore it has negative influence in
profitability which is in consistent with the result of multiple regression.
4.2.6: Breweries Sub-Sector
Based on data availability, the selected firms in this sub-sector include; Guinness Nigeria plc and Nigeria
Breweries plc.
Table 4.2.6 presents the descriptive statistics of Breweries sub-sector of manufacturing firms in Nigeria.
Table 4.2.6: Descriptive statistics showing minimum, maximum, mean, standard deviation and
variance values of variables for the Breweries Sector
N Minimum Maximum Mean Std. Deviation Variance
Profit 24 .1358 5.3059 1.346018 1.3698844 1.877
ACCOUNTS RECEIVABLE
24 .0068 .3512 .078270 .0692723 .005
INVENTORY 24 .0306 7.7806 .796092 1.6481589 2.716
ACCOUNTS PAYABLE
24 .0793 4.6114 .950503 .9878821 .976
CASH CONVERSION
24 -1.9461 5.6375 -.095290 1.3365166 1.786
LIQUIDITY 24 .6766 1.6857 1.158782 .3285677 .108
DEBT 24 .0000 .0330 .002641 .0077337 .000
SALES GROWTH 24 -89.4971 49.2500 15.602198 26.4026953 697.102
Valid N (listwise) 24
Source: computed from Handpicked Data from the Annual Reports and Accounts of 2 quoted companies, 2000 - 2011, and the Fact book, 2010/2011 for firms in the Breweries Sector
The results in table 4.2.6 show that in spite of average sales growth rate of 156% and mean =1.3460),still the
receivable average rate is low with mean of 0.08. This shows that they recover their cash, and this also
shows that bad debt may be less or may not occur at all.
81
The rate of inventories/stock is 80%(mean =0.7961). The stocks this sub-sector were keeping were too
much. There may be fall in price and some may become obsolete or expire if they are not sold. What is
difficult to understand is that even when their turnover rate is 80%. Still they were making sales of
mean15.6022. Their liquidity ratio is not too good, it is 1.2 which is not enough for the sub-sector to pay off
their obligations. They did not borrow any debt during this period under study. CCC rate had negative rate.
This implies that this sub-sector is not quick at all in investing cash by buying stocks which would be sold
and be received as cash for further investments.
They are owing their suppliers at the average rate of .9505, which is far more than their receivable. This sub-
sector is not managing their working capital well even though they made profit of 1.3460(mean) during the
years under study.
4.2.7: Packages Sub-Sector
Based on data availability the two (2) firms under this sub-sector were selected. They firms include: Avon
Crown caps and containers Plc, and Beta Glass Plc
Table 4.2.7: Descriptive statistics showing minimum, maximum, mean, standard deviation and variance values
of variables for the Packages Sector
N Minimum Maximum Mean Std. Deviation Variance
Profit 24 -.2065 .5721 .098400 .1459366 .021
ACCOUNTS
RECEIVABLE
24 .0604 .7539 .281783 .2395415 .057
INVENTORY 24 .0116 .6825 .356289 .1965180 .039
ACCOUNTS
PAYABLE
24 .0293 1.4671 .632448 .4052653 .164
CASH CONVERSION
CYCLE
24 -.7940 .5964 -.118196 .3615412 .131
LIQUIDITY 24 .5905 11.3340 1.669695 2.1691181 4.705
DEBT 24 .0000 1.0853 .081367 .2332144 .054
SALES GROWTH 24 -99.9059 167196.100
0
7193.9654
43
3.4096905E4 1.163E9
Valid N (listwise) 24
Source: computed from Handpicked Data from the Annual Reports and Accounts of 2 quoted companies, 2000 -
2011, and the Factbook, 2010/2011 for firms in the Packages Sector
It could be seen from table 4.2.7, the average receivable of 28%, and (mean = 0.2818) while that of payables
is 63% mean = 0.6324, this shows that they are owing their suppliers [creditors] more than those customers
82
[debtors] that are owing them during the period of this study. Their sales growth rate is more than 100%
even when their receivable is low. This sub -sector did not do well in the results of their operation
(profitability) mean = .0984, inspite of the fact that they have a liquidity ratio of 1.67 approximately 2. This
shows that they have cash to settle their obligations. Their external long term debt is minimal.
4.2. 8 Automobile and Tyre Sub-Sector
The descriptive statistics of firms in the automobile and Tyre sub-sector has only two quoted
companies Based on the availability of data ,only Incar Nigeria Plc was selected. Table 4.2.8 is
the descriptive statistic of Incar company Plc, in the Automobile and Tyre Sub-sector.
Table 4.2.8: Descriptive statistics showing minimum, maximum, mean, standard
deviation and variance values of variables for the Automobile and Tyre Sector
N Minimum Maximum Mean Std. Deviation Variance
Profit 12 -.1704 .5737 .094055 .2167932 .047
ACCOUNTS RECEIVABLE
12 .1080 5.1820 .989518 1.6569999 2.746
INVENTORY 12 .2160 1.2736 .414478 .2832635 .080
ACCOUNTS PAYABLE
12 .2554 1.7063 .619115 .4159961 .173
CASH CONVERSION CYCLE
12 -1.0704 5.0477 .686525 1.8399560 3.385
LIQUIDITY 12 .5237 16.2012 3.518897 4.2903793 18.407
DEBT 12 0 0 .03 .063 .004
SALES GROWTH 12 -51.2000 291.0000 57.942152 108.5718920 11787.856
Valid N (listwise) 12
Source: computed from Handpicked Data from the Annual Reports and Accounts of 1 quoted company,
2000 - 2011, and the Factbook, 2010/2011 for firms in the Automoblie and Tyre Sector
Results show that the results of operations (profitability) of Incar Plc is not effective but the liquidity ratio is
very comfortable ratio. The rate is high and this makes it possible for the company to have enough cash to
settle their financial obligations. This company made huge sales and they have also high rate of receivables.
This means that they manage their sales growth. The only problem here is if care is not taken, those
receivables may not be fully recovered. Inspite of high rate of receivables, sales growth and liquidity, profit
rate is still low below 10%.This is not encouraging. The debt ratio is 0.03, which is not too much for them to
83
pay since their liquidity position is encouraging. They are not keeping too much stock, this is as a result of
high rate of their sales.
4.2.9 A Cross Sub-Sector Comparison
Table 4.2.9: A cross sector comparison of mean, maximum and minimum values of variables in food and beverages sector.
Variables Measures Seven Up Cardbury FlourMills Nestle Nigeria Bottling Company
Profitability Mean Maximum Minimum
0.118382 0.2719 0.0113
0.1811 0.2576 -0.1942
0.0864 0.1703 0.0000
0.3863 0.5427 0.0482
0.1279 0.3935 0.0415
Account Receivable
Mean Maximum Minimum
0.100489 0.1171 0.0825
0.1811 0.3064 0.1055
0.1493 1.0597 0.0296
0.0541 0.1050 0.0287
0.1160 0.8135 0.0029
Stock Turnover
Mean Maximum Minimum
0.2898 0.3550 0.2265
0.2973 0.5698 0.1167
0.1663 1.0597 0.0296
0.2099 0.2947 0.0297
0.2513 0.4379 0.1582
Account payable
Mean Maximum Minimum
0.3455 0.6305 0.0460
0.3477 0.9308 0.1291
0.2997 0.7765 0.000
0.0811 0.1320 0.0411
0.2525 0.3403 0.0244
Cash Conversion Cycle
Mean Maximum Minimum
-0.3186 0.3525 -0.0473
0.0825 0.3951 -0.4427
-0.0558 0.8965 -0.5442
0.1440 0.2922 -0.2175
-0.0353 0.1914 -0.4267
Liquidity Mean Maximum Minimum
1.1432 1.4472 0.9869
1.2528 1.8619 0.3390
0.9944 1.3273 0.6679
1.2359 1.5797 0.9374
0.8781 1.2216 0.5978
Debt Mean Maximum Minimum
0.1091 0.2448 0.000
0 0.02 0
0.1468 1.0000 0.0000
0.0393 0.2050 0.0000
0.2547 1.3454 0.0015
Sales Growth Rate.
Mean Maximum Minimum
3527.64 26064.26 -98.9200
3.5231 32.9600 -89.4000
21.9332 41.0500 -2.4300
21.4179 41.0700 0.0000
225.72 2606.00 0.0000
Source: Researchers Compilation based on SPSS computation, version 17.1 Analytical software
From the Above table, all the companies in food and beverages sub-sector did not perform well, their
profitability ratio did not reach up to average of 50%. This shows that their results of operation is not
satisfactory. Nestle was the highest in making profit with average mean of 0.38.
Cadbury had the highest mean of 0.18 which is low under accounts receivables. This shows that
What the firms under this sub-sector would receive during the period under study is not too much. It also
shows that they are receiving cash from their debtors and when they make sales their customers pay.
Coming to that of stock turnover Cadbury had the highest mean of 0.30, followed by seven –up (mean0.29),
Nigeria Bottling company(mean=.25 and Nestle (mean=0.21. All these companies did not have a lot of
stocks unsold. This shows that they would not have obsolete or expired stocks during this period under
study. The average rate of accounts payable of seven-up Nigeria plc is the highest mean=0.35. This shows
that they had more creditors to settle than other companies under this sector, followed by flour mills, then
84
others. Nigeria Bottling company has a negative mean of -0.04 under CCC, which is the lowest followed by
Flour mills. This indicates that the rate of their flow of cash from suppliers to inventories, receivables and
back into cash is very low.Cadbury plc has the highest mean of 1.25 under liquidity, followed by Nestle,
seven-up and then flour mill. Nigeria bottling company had the lowest mean. This shows that food and
beverages company is not liquid enough to settle their financial obligations. None of these companies has up
to 2 as mean. Their borrowings are not high especially Cadbury and Nestle Plc.
Table 4.2.10: Cross sectional comparison of Minimum maximum and mean values of varieties in industrial and domestic product sub- sector.
Variables Measures Aluminum & Extrusion
BOC Gases
First Aluminum
Nigeria Enamel
Vita Foam
Vono Products
Profitability Mean Maximum Minimum
0.04115 0.1831 -0.1745
0.4540 2.1378 0.0703
0.0046 0.0602 -0.0750
0.0543 0.1214 0.0279
0.6858 4.9160 0.0891
-0.0945 0.0628 -0.4896
Account Receivable
Mean Maximum Minimum
0.2427 2.2409 0.0020
1.621 1804.00 0.1138
0.2400 1.4753 0.0132
0.2941 2.7925 0.0139
0.1089 0.7015 0.0194
0.2666 0.7996 0.0926
Stock Turnover
Mean Maximum Minimum
0.2078 0.4702 0.0279
0.4310 1.1094 0.2047
0.3654 0.0475 0.2435
0.2341 0.2981 0.1326
1.0874 8.0195 0.1696
0.4406 2.2894 0.0934
Account payable
Mean Maximum Minimum
0.1876 0.7225 0.0138
0.9068 4.7169 0.0709
0.5329 0.4757 0.2435
0.3784 0.7590 0.0100
0.4267 0.6538 0.1070
1.4721 3.4276 0.5541
Cash Conversion Cycle
Mean Maximum Minimum
0.2404 2.3394 -0.2188
-0.0034 0.5090 -0.6932
0.0069 1.2540 -0.4449
0.1410 2.621 -0.5114
0.6421 7.4138 -0.8964
-1.0823 0.2046 -4.5598
Liquidity Mean Maximum Minimum
0.5908 1.0795 0.2741
1.5682 2.3405 0.6459
0.9842 1.1440 0.7433
2.1074 12.3836 0.8756
1.4361 1.6264 1.2592
0.7875 1.4464 0.2734
Debt Mean Maximum Minimum
0.1288 0.5744 0.0000
0.01 0.0 0.0
0.05 0 0
0.00 0 0
0.4491 3.1698 0.0037
0.0160 0.1927 0.0000
Sales Growth Rate.
Mean Maximum Minimum
13.0675 109.34 -99.88
12.7959 944.62 -88.940
76.7719 906.80 -89.31
81.1229 985.50 -90.790
37.58 946.02 -607.00
25.811 365.30 -55.140
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software
Generally Industrial and domestic products sub-sector did not perform well in their results of operation. It is
only vita foam that has up to 69 average rate, others do not have up to 50%.
B.O.C. Plc has the highest mean of 1.62 in their receivables, while others have low average rate of not up to
50%. This shows that their receivable rate generally is also low. Therefore, it implies that they do not have
too much to receive thereby avoiding bad debt. It is only vita foam that has the highest mean of 1.09 as their
stock turnover, others also have less than 50% average rate of stock turnover. This indicates that their
turnover rate is not too bad, because if they have too high rate of stocks, obsolesce may be experienced. Vita
foam should watch it. The only thing is that their kind of product does not expire easily. Vita foam also has
the highest mean of 0.89 in CCC. The highest liquidity mean goes to Nigeria Enamelware plc (2.11). This
85
shows that Nigeria plc is liquid enough to settle its debts, even when they did not borrow at all. Their
payable average rate is also high with mean of 1.47 for vono, then vita foam mean 0.43, Aluminum
Extrusion, Vono products and First Aluminum did not have too much to pay,B.O.C had up to0.91as mean
which is too much. This sub-sector did not have enough cash to settle their obligations during the period
under study.
Companies under this sector did not borrow so much. Nigeria Enamelware, and Vono products did not
borrow at all during this period. Their sales growth mean is very high except B.O.C case that has sales
growth mean of 12.80, which is the lowest. Generally this sub-sector made huge sales and do not have too
much to receive. This implies that their customers are paying, though their profit average rates are very low.
Table 4.2.11: Cross section of specific comparison of minimum maximum and mean values of variables in the Heath sector
Variables Measures Evans Medical May & Baker Pharma-Deko Plc Profitability Mean
Maximum Minimum
-0.0102 0.0659 -0.2417
0.0867 0.1809 0.0452
-0.0356 0.2000 -0.2896
Account Receivable Mean Maximum Minimum
0.1931 0.3481 0.0207
0.1754 0.3082 0.1023
2.5045 27.880 0.0225
Stock Turnover Mean Maximum Minimum
0.6572 1.0412 0.4496
0.4297 0.5923 0.2579
0.6460 3.1212 0.0682
Account payable Mean Maximum Minimum
0.6514 0.5503 0.1773
0.2452 0.7091 0.0614
1.6762 3.9085 0.4533
Cash Conversion Cycle
Mean Maximum Minimum
0.5387 0.8333 -0.8681
0.2317 0.5386 -0.4001
-0.6414 1.0095 -2.1581
Liquidity Mean Maximum Minimum
0.9885 1.4836 0.6094
1.7150 2.3346 0.7131
0.6165 1.2224 0.2048
Debt Mean Maximum Minimum
0.00 0 0
0.0570 0.2531 0.0000
0.05858 0.3614 0.0000
Sales Growth Rate. Mean Maximum Minimum
16.826 54.04 -21.080
16.6292 71.290 -15.360
114.81 691.730 -85.380
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software
Evans medical and Pharma –Deko plc did not make any profit during the period of this study. May and
Baker’s profit average rate is not up to10%. This shows that the financial performance of this sub-sector
during the period under study is nothing to write home about. Generally they did not perform well. Their
average receivables rate is low, only Pharma – Deko has mean of 2.50 as its receivables. This means that
86
Pharma Deko should be careful otherwise, they may end up having bad debt. This is dangerous. Stock
turnover rate is low in May and Baker with mean of 0.43, while other companies have above average. This
means that they have so much stock unsold during this period. Pharma Deko’s CCC is negative while only
Evans has more than 50% average rate. This shows that generally they not do well in their conversion of
their stocks into cash. This sub-sector do not have enough cash to settle their debts. Evans and Pharma –
Deko have much to pay to its creditors even when they did not make profit, they have liquidity mean of 0.99
and 0.62 respectively which is low.
May and Baker have reasonable cash to work with (mean 1.72), while Evans medicals do not have enough
cash to work with. This shows that Evans made the highest sales during this period, it does not have enough
cash even when it makes too much sales, and it made no profit up to five years. This is very bad, even when
they are listed in the Nigeria stock exchange, though they did not borrow. The same is applicable to Pharma
Deko Plc, it made profits in few years and none in other years see appendix 16.
Table 4.2.12: A cross section comparison among the minimum, maximum and mean value of variables in Building materials and chemical sector
Variables Measures Benue Cement Berger Paints Premier Profitability Mean
Maximum Minimum
42.3392 509.000 -0.4860
0.0919 0.1995 -0.0330
-0.0576 0.0596 -0.3360
Account Receivable Mean Maximum Minimum
0.4185 0.7005 0.0000
0.1197 0.3044 0.0150
0.1574 0.2691 0.0757
Stock Turnover Mean Maximum Minimum
3.7920 18.980 0.0000
0.4230 0.7759 0.2144
0.1483 2.197 0.0758
Account payable Mean Maximum Minimum
5.0196 15.712 0.0000
0.3269 0..8878 0.0980
0.3893 0.9188 0.0431
Cash Conversion Cycle
Mean Maximum Minimum
-5.9446 0.3866 -30.479
0.0970 0.7446 -0.4720
-0.1129 0.2801 -0.9259
Liquidity Mean Maximum Minimum
0.1999 0.4349 0.0412
1.4997 3.7050 0.1740
0.9429 2.7261 0.2359
Debt Mean Maximum Minimum
0.0703 0.3249 0.0000
0.0 0.0 0.00
0.0735 0.4579 0.0000
Sales Growth Rate. Mean Maximum Minimum
9.4174 100.00 -47.72
118.100 1271.30 -6.6112
-3.3208 26.2900 -99.900
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software. Building materials and chemical Sub-Sector generally did not perform well. Premier paints did not perform
well and it has a negative mean of – 0.06. This implies that it did not make profit at all. It only made profit
in few years but no profit in more years under study. Receivables average rate of this sub-sector is low,
87
while only Benue Cement has 5.02 mean on payables. This shows that Benue Cement has a lot to settle even
when they did not make enough profit, their liquidity ratio is not up to 2. They cannot pay their creditors
because they do not have enough cash, even though they did not borrow. They should also watch their sales
growth, the average rate is too high to avoid bad debt. This may also be one of the reasons why they are not
liquid enough and they do not make enough profit. Berger paints followed by premier paints did better by
having liquidity mean of 1.50 and 0.94 respectively, still they did not make up to 2 which is a more
comfortable ratio when using current ratio. CCC is low and receivables rate is also low in this sub-sector
under the period of study.
Table 4.2.13: A Cross section comparison of minimum, maximum and mean values of variables in the
Breweries sub-sector
Variables Measures Guinness Nigerian Breweries Profitability Mean
Maximum Minimum
2.0193 3.2260 0.2421
0.6727 5.3059 0.1358
Account Receivable Mean Maximum Minimum
0.0768 0.1466 0.0068
0.0796 0.3512 0.0219
Stock Turnover Mean Maximum Minimum
036619 3.7257 0.3831
0.4302 7.7806 0.0306
Account payable Mean Maximum Minimum
0.9658 4.6114 0.3831
0.9351 2.2747 0.0793
Cash Conversion Cycle
Mean Maximum Minimum
-0.3177 0.0782 -0.7786
-0.1272 5.6375 -1.9461
Liquidity Mean Maximum Minimum
1.4021 1.6857 1.2141
0.9153 1.6217 0.6766
Debt Mean Maximum Minimum
0.0025 0.0157 0.0000
0.0027 0.0330 0.0000
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software Guinness breweries performed better than Nigeria Bottling Company. With means of 2.02 and 0.67
respectively. The sales growth of Guinness is very high and their rate of stock /inventory is too low. While
their receivable is also very low. Guinness has much to pay. This implies that they can be able to pay their
creditors since they made enough profit. CCC is also low with negative means of -0.32 and -0.13. This also
shows that the rate of their inflow of cash is low. Both companies did not borrow.
88
Table 4.2.14: A Cross section comparison of minimum, maximum and mean values of variables in the Packages sub-sector
Variables Measures Avon Plc BETA GlASS Plc Profitability Mean
Maximum Minimum
0.1075 0.5721 0.0196
0.0892 0.3455 -0.2065
Account Receivable Mean Maximum Minimum
0.4531 0.7539 0.0116
0.1104 0.1802 0.0604
Stock Turnover Mean Maximum Minimum
0.2499 0.5659 0.0116
0.4626 0.6825 0.2482
Account payable Mean Maximum Minimum
0.4334 0.7037 0.0293
0.8314 1.4671 0.0796
Cash Conversion Cycle Mean Maximum Minimum
0.1109 0.5964 -0.4566
-0.3472 0.0471 -0.7940
Liquidity Mean Maximum Minimum
2.2665 11.3340 0.6175
1.0728 2.3999 0.5905
Debt Mean Maximum Minimum
0.1493 1.0853 0.0014
0.01 0.0 0.0
Sales Growth Rate. Mean Maximum Minimum
4.9043 5224.00 -9.18
1.394302E4 167196.10 -99.9059
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software
Companies under packages sub-sector did not perform well in the results of their operation (profitability).
Avon made the highest sales mean of 4,91 while Beta Plc made mean of 1.40. Even though Avon did not
make enough profit, it is liquid enough to settle its obligations with liquidity mean of 2.27. This is very
comfortable. This Sub-sector did not borrow. Beta Plc did not do well in so many areas. For instance it, did
not make enough profit, it is not liquid enough to settle its obligations, and it is owing up to 83%, to his
creditors. It also has negative mean of
–0.35 of its CCC. Rate of Stocks turnover is low.
Table 4.2.15: A Cross section comparison of minimum, maximum and mean values of variables in the Automobile and Tyre sector
Variables Measures INCAR Variables Profitability Mean
Maximum Minimum
0.0940 0.5737 -0.1704
Profitability
Account Receivable Mean Maximum Minimum
0.9895 5.1820 0.1080
Account Receivable
89
Continued from table 4.2.15:
Stock Turnover Mean Maximum Minimum
0.4144 1.2736 0.2160
Stock Turnover
Account payable Mean Maximum Minimum
0.6191 1.7063 0.2554
Account payable
Cash Conversion Cycle Mean Maximum Minimum
0.6865 5.0477 -1.0704
Cash Conversion Cycle
Liquidity Mean Maximum Minimum
3.5188 16.2012 0.5237
Liquidity
Debt Mean Maximum Minimum
0.03 0 0
Debt
Sales Growth Rate. Mean Maximum Minimum
57.942 291.00 -51.200
Sales Growth Rate.
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software. This sub-sector has only two companies quoted in Nigeria stock exchange. Due to availability of data only
Incar was selected. Results based on the descriptive analysis show that the financial performance of Incar
Nigeria plc is not effective considering its mean of return on assets of 0.09. This company, made huge sales
of mean = 57.94 and would receive mean = 0.99. This shows that they collect cash as at when due from their
debtors since they have this little to collect inspite of their huge sales. Incar Plc is liquid enough during this
period of study with mean= 3.52. This shows that they can pay their obligations very well. The average rate
of CCC was 69%, this also shows that this company can convert their stocks into cash within this period
easily. Since they have enough cash, they would be able to settle their creditors and the little loan they
borrowed outside. Even though this company did not make enough profit, is liquid enough to settle its bills.
Table 4.2.16: Mean values of variables of Foods & Beverages, Industrial/ domestic products, Health, Building Materials/Chemicals , Breweries, packages as well as Automobiles & Tyre Sub sectors.
VARIABLES FOOD & BEVERAGES
INDUSTRIES & DOMESTIC PRODUCTS
HEALTH
BUILDING MATERIALS
BREWERIES
PACKAGES
AUTO
MOBILE
&TYRE
PROFITABILITY
0.1606 0.2162 0.0185 0.0677 1.3040 0.1045 0.0479
ACCOUNT RECEIVED
0.1277 0.3363 1.1121 0.2276 0.0612 0.3083 1.1598
90
Continued from table 4.2.16:
STOCK TURN OVER
0.2506 0.4855 0.6088 0.5006 0.9069 0.3532 0.4399
ACCOUN PAYABLE
0.2812 0.1684 0.1029 -0.2851 -0.1782 -0.0843 0.9958
LIQUIDITY RATIO
1.1123 1.2900 1.1681 1.8927 1.1776 1.6523 3.6875
CASH
CONVERSION
CYCLE
0.0827 0.1684 0.1029 -0.2851 -0.1782 -0.0843 0.9958
DEBTS 0.0744 0.1263 0.0152 0.0375 0.000 0.0953 0.0323
SALES GROWTH RATE
9.0909 46.08 53.48 49.69 20.75 8.373 66.83
Source: Researchers Compilation based on SPSS computation, Version 17.1 Analytical software.
Generally the seven (7) Sub-sectors in manufacturing sector did not perform well. They did not make
enough profit. It is only companies in Breweries that made profit of 1.30 (mean) while others did not
perform well at all. Health has the lowest profitability mean of 0.019. Automobile has the highest mean of
1.16 of the receivables, while others have low mean of between 0.06 and 0.34 respectively. Generally these
sub-sectors have low receivable means and also payable means. Building materials, Breweries and
packaging have little or nothing to pay to their creditors. Others have little to pay. The most liquid of all
seven (7) Sub-sectors is Automobile and Tyre sub-sector, followed by building materials, packages,
Industrial/Domestic products Plc, Breweries, Health and Food and Beverages Plc. This means that only
Auto-mobile and Tyre Sub-sector has enough cash to settle its obligations. Building materials and packages
if approximated their mean will be 2 which is okay, while others cannot be able to settle their obligations
because they do have enough cash, Breweries Sub-sector did not borrow while others borrowed little.
Automobile and Tyre has the highest sales growth means of 66.83 while Health has means =53.48. the
lowest of all is package with mean of 8.37. Breweries had the highest mean of 0.91 of stock turnover
followed by Health then others. Generally, they do not keep too much stock so this help them to avoid
obsolete stocks. The rate by which they turn their stocks into cash is also low .We can see that this
descriptive analysis support the multiple regression results that even though some of these companies are
liquid during the period under study they did not make enough profit. That is to say, for instance that
liquidity has negative impact/influence on the profitability of Nigerian manufacturing firms in Nigeria, and
also receivable has positive relationship with profitability of the companies under study and so on.
91
4.3 . Correlation Matrix.
Table 4.3: Correlation Matrix of Pooled variables in the Twenty two firms considered in the study
PROFIT AR INVENTORY AP CCC LIQUIDITY DT SL
PROFIT Pearson Correlation 1
Sig. (2-tailed)
N 262
AR Pearson Correlation -.001 1
Sig. (2-tailed) .985
N 262 262
INVENT Pearson Correlation -.016 -.013 1
Sig. (2-tailed) .792 .831
N 262 262 262
AP Pearson Correlation .024 -.009 .705** 1
Sig. (2-tailed) .693 .883 .000
N 262 262 262 262
CCC Pearson Correlation -.015 .018 -.770** -.687** 1
Sig. (2-tailed) .813 .774 .000 .000
N 262 262 262 262 262
LIQUIDI
TY
Pearson Correlation -.047 .046 -.065 -.119 .175** 1
Sig. (2-tailed) .444 .459 .298 .055 .005
N 262 262 262 262 262 262 262
DT Pearson Correlation -.011 -.019 -.042 -.046 .011 -.044 1
Sig. (2-tailed) .860 .755 .498 .458 .858 .482
N 262 262 262 262 262 262 262
SL Pearson Correlation -.005 -.005 -.009 .001 .000 -.020 -.015 1
Sig. (2-tailed) .936 .938 .887 .990 .994 .749 .815
N 262 262 262 262 262 262 262 262
**. Correlation is significant at the 0.01 level (2-tailed).
Source: Computes from data in appendixes 1-22 (using Version 17.1 Analytical software Computation)
The correlation between sales growth and profitability is negative but significant. This is in consistent with
the result of descriptive statistics of all the seven sub-sectors in Nigerian manufacturing firms.
The correlation between debt and account receivable is negative and significant. This finding validates our
prior position that receivable rate of the companies under study is low and also their debt rate is also low.
Their debtors are paying even when some of the companies did not borrow as all. The correlation between
accounts payable, liquidity, stock/inventory and profitability is negative but significant. This also supports
the finding in the multiple regression analysis that STO, AP and LQ have negative and significant
relationship with the profitability of the companies under study. The correlation between accounts receivable
92
and CCC is positive and also significant. This implies that as the receivables of these companies decrease
their CCC also decreases Even though these companies have positive and negative correlation between each
other, almost all are significant.
4.3.1 Discussion of sub-sector Results [Regression Analysis].
This part of the study discusses the regression results of the seven (7) sub-sectors.
4.3.1 Food and Beverages
Table 4.3.1: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and
SL of Food and Beverages firms in Nigeria
Variables
Linear Regression
Semi Log Regression Double Log Regression Exponential Regression
Constant -0.018 (-0.210)
0.017 (0.224)
-0.633*** (-2.706)
-0.565* (-1.789)
Accounts Receivable Ratio (AR)
0.034 (0.253)
-0.051 (-1.085)
-0.223 (-1.520)
-0.277 (-0.579)
Stock Turnover Ratio (STO) .375* (1.742)
0.092 (-1.085)
0.661** (2.435)
0.487 (0.629)
Accounts Payable Ratio (AP) -0.382*** (-2.896)
-0.143*** (-2.766)
-0.042 (-0.261)
-0.432 (-0.909)
Cash Conversion Cycle Ratio (CCC)
-0.070 (-0.658)
-0.041 (-0.848)
-0.299* (-1.990)
0.104 (0.272)
Liquidity Ratio (LQ) 0.178*** (2.893)
.472*** (3.755)
-0.158 (-0.389)
-0.131 (-0.589)
Debt Ratio (DT) (Control) -0.089 (-1.060)
0.001 (0.019)
.172** (2.121)
-0.196 (-0.650)
Sales Growth Rate (SL) (Control)
4.332E-6 (0.905)
0.015 (0.738)
-0.008 (-0.131)
-1.167E-5 (-0.677)
R2 0.367 0.454 0.306 0.063
Adjusted R2 0.281 0.381 0.212 -0.063
F-Ratio 4.299*** 6.181*** 3.270*** .502
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variables influencing the profitability ratio of Food and
Beverages firms in Nigeria were summarized in Table 4.3.1 above. Out of the four functional models of the
multiple regression calculated, the semi log model was chosen because it has the highest number of
significant variables as well as a very significant F-ratio (6.181***) value which indicated that the choice
model suited the analysis. Furthermore, the results of the analysis revealed an R2 value of 0.454 thus
indicating that 45.4% variation in the profitability ratio (dependent variable) of Food and Beverages firms in
93
Nigeria was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that STO and LQ had significant positive relationships with the industries’
profitability ratio at 1% level of significance. This implies that a unit increase in values of STO and LQ shall
bring about corresponding increases in the profitability ratio of Food and Beverages firms in Nigeria. On the
other hand, the industries’ AP and CCC had significant but negative relationships with the profitability ratio
at 1% levels of significance. This means that unit increases in the variables shall bring about corresponding
decrease in the profitability ratio of Food and Beverages firms in Nigeria. However, the AR of the firms has
negative and non- significant relationship with their profitability ratio
4.3.2 Industrial and Domestic Products Firms
Table 4.3.2: Multiple Regression Analysis showing the relationship between Profitability ratio and
AR, STO, AP, CCC, LQ, DT and SL of Industrial and Domestic Products firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.143** (-2.580)
0.234 (1.262)
-0.622*** (-3.428)
-0.753*** (-7.273)
Accounts Receivable Ratio (AR)
0.001*** (5.371)
0.026 (0.234)
0.081 (0.754)
0.000 (1.317)
Stock Turnover Ratio (STO)
-0.130*** (-2.816)
-0.323 (-1.146)
-0.096 (-0.347)
-0.009 (-0.106)
Accounts Payable Ratio (AP)
0.265*** (5.282)
0.115 (0.670)
0.049 (0.290)
0.155 (1.662)
Cash Conversion Cycle Ratio (CCC)
0.136*** (3.617)
0.068 (0.649)
-0.011 (-0.109)
-0.059 (-0.846)
Liquidity Ratio (LQ) 0.033 (1.417)
0.619* (1.906)
-0.547* (-1.720)
0.009 (0.217)
Debt Ratio (DT) (Control)
1.469*** (17.395)
0.126 (1.057)
.286** (2.449)
0.125 (0.793)
Sales Growth Rate (SL) (Control)
5.505E-5 (0.396)
-0.044 (-0.462)
0.006 (0.069)
0.000 (0.399)
R2 0.846 0.113 0.143 0.133
Adjusted R2 0.830 0.016 0.049 0.038
F-Ratio 50.357*** 1.161 1.524 1.406
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
94
The results of multiple regression analysis for the variables influencing the profitability ratio of Industrial
and Domestic products firms in Nigeria were summarized in Table 4.3.2 above. From the results it could be
observed that out of the four functional models of the multiple regression calculated, the Linear Regression
model was chosen because it has the highest number of significant variables as well as a very significant F-
ratio (50.357***) value which indicated that the model chosen best fitted the analysis. Furthermore, the
results of the analysis revealed an R2 value of 0.846 thus indicating that 84.6% variation in the profitability
ratio (dependent variable) of Industrial and Domestic products firms in Nigeria was accounted for by the
explanatory (independent) variables considered in the analysis. Specifically the results showed that AR, AP
and CCC had significant positive relationships with the industries’ profitability ratio at 1% level of
significance. This implies that a unit increase in values of AR, AP and CCC shall bring about corresponding
increases in the profitability ratio of Industrial and Domestic products firms in Nigeria. On the other hand,
the industries’ STO had significant but negative relationship with the profitability ratio at 1% levels of
significance. This means that unit increase in the variable shall bring about corresponding decrease in the
profitability ratio of Industrial and Domestic products firms in Nigeria. However, the LQ of the firms has
positive but non- significant relationship with their profitability ratio.
4.3.3: Health Care Firms Table 4.3.3: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Health Care firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.102* (-1.732)
-0.048 (-0.923)
-0.690* (-1.867)
0.124 (0.417)
Accounts Receivable Ratio (AR)
0.000 (-0.050)
0.057 (1.357)
-0.043 (-0.146)
0.008 (0.238)
Stock Turnover Ratio (STO)
0.039 (0.0617)
0.071 (1.217)
-0.271 (-0.657)
-0.024 (-0.076)
Accounts Payable Ratio Ratio (AP)
-0.024 (-0.439)
-0.027 (-0.494)
-0.078 (-0.200)
-0.166 (-0.419)
Cash Conversion Cycle Ratio (CCC)
0.019 (0.0345)
-0.078 (-1.018)
-0.478 (-0.883)
-0.023 (-0.084)
Liquidity Ratio (LQ) 0.089** (2.422)
0.138 (1.503)
-0.727 (-1.122)
-0.502 (-2.680)
Debt Ratio (DT) (Control)
-0.092 (-0.417)
-0.043 (-1.037)
0.088 (0.302)
1.024 (0.918)
95
Continued from table 4.3.3:
Sales Growth Rate (SL) (Control)
0.000* (1.800)
0.087 (4.088)
-0.160 (-1.062)
0.000 (0.418)
R2 0.469 0.579 0.147 0.337
Adjusted R2 0.336 0.474 -0.066 0.172
F-Ratio 3.532*** 5.510*** 0.688 2.036*
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables The results of multiple regression analyses for the variables influencing the profitability ratio of Healthcare
firms in Nigeria were summarized in Table 4.3.3 above. The Results showed that out of the four functional
models of the multiple regression calculated, the Semi log Regression model was chosen because it has the
highest number of significant variables as well as a very significant F-ratio (5.510***) value which indicated
that the choice model best fitted the analysis. Also, the results of the analysis revealed an R2 value of 0.579
thus indicating that 57.9% variation in the profitability ratio (dependent variable) of Healthcare firms in
Nigeria was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that AR, STO and LQ had significant positive relationships with the
industries’ profitability ratio at 1% level of significance. This implies that a unit increases in values of AR,
STO and LQ shall bring about corresponding increases in the profitability ratio of Breweries industries in
Nigeria. On the other hand, the industries’ AP and CCC had negative and non-significant relationship with
the profitability ratio at 1% levels of significance. This means that unit increase in the value of AP and CCC
shall bring about corresponding decrease in the profitability ratio of Healthcare industries in Nigeria.
4.3.4: Building Materials, Chemicals and Paints
Table 4.3.4: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of Building Materials, Chemicals and Paints firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 32.226 (0.802)
-26.319 (-0.446)
-0.578 (-1.077)
-0.541** (-2.078)
Accounts Receivable Ratio (AR)
48.276 (0.364)
-4.002 (-0.068)
0.143 (0.266)
0.213 (0.248)
96
Continued from table 4.3.4:
Stock Turnover Ratio (STO)
-31.968 (-1.111)
-33.712 (-0.966)
-0.365 (-1.150)
0.000 (0.002)
Accounts Payable Ratio (AP)
0.515 (0.062)
-22.050 (-0.475)
0.238 (0.564)
-0.032 (-0.594)
Cash Conversion Cycle Ratio (CCC)
-17.228 (-1.071)
27.867 (0.831)
0.238 (0.783)
-0.028 (-0.265)
Liquidity Ratio (LQ) -13.399 (-0.651)
-6.962 (-0.156)
-0.282 (-0.693)
0.006 (0.047)
Debt Ratio (DT) (Control)
-129.112 (-0.806)
-52.295 (-1.723)
-0.215 (-0.781)
0.870 (0.838)
Sales Growth Rate (SL) (Control)
0.000 (-0.006)
-2.038 (-0.109)
-0.082 (-0.482)
0.000 (-1.100)
R2 0.086 0.212 0.293 0.134
Adjusted R2 -0.142 0.015 0.116 -0.082
F-Ratio 0.378 1.075 1.616 0.621
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables The results of multiple regression analysis for the variable influencing the profitability ratio of automobile
and tyre industries in Nigeria were summarized in Table 4.3.4. Out of the four functional models of the
multiple regression calculated, the Double log regression model was chosen because it has the highest
number of significant variables as well as a very significant F-ratio(1.616***) which indicated that the
choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2 value of 0.293 thus
indicating that 29.3% variation in the profitability ratio (dependent variable) of Building Materials,
Chemicals and Paints Industries in Nigeria was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically, the results showed that STO and LQ variables had significant
negative relationships with the industries’ profitability ratio at 1% level of significance. This implies that a
unit increases in STO and LQ values shall bring about corresponding decrease in the profitability ratio of
Building Materials, Chemicals and Paints Industries in Nigeria. On the other hand, the industries’ AR,CCC
and AP had significant and positive relationships with the profitability ratio at 1% levels of significance.
This means that unit increases in the variables shall bring about corresponding increase in the profitability
ratio of the industries in Nigeria.
97
4.3.5: Brewery firms
Table 4.3.5: Multiple Regression Analysis showing the relationship between Profitability and AR,
STO, AP, CCC, LQ, DT and SL of Brewery firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -1.286 (-1.274)
1.756* (1.985)
-1.868*** (-3.381)
-1.436*** (-3.829)
Accounts Receivable Ratio (AR)
8.085 (0.934)
-1.220 (-0.825)
-0.928** (-2.128)
2.944 (0.915)
Stock Turnover Ratio (STO)
-0.049* (-0.065)
-.124 (-0.047)
0.526 (1.696)
0.047 (0.167)
Accounts Payable Ratio (AP)
0.111 (0.143)
1.930* (1.999)
-0.616 (-1.568)
-0.040 (-0.141)
Cash Conversion Cycle Ratio (CCC)
-0.165 (-0.213)
2.309 (1.175)
-0.139 (-0.389)
-0.132 (-0.460)
Liquidity Ratio (LQ) 2.088** (2.870)
1.866 (1.050)
-0.822 (-0.835)
1.088*** (4.023)
Debt Ratio (DT) (Control)
-43.374 (-0.546)
1.316* (1.822)
0.055 (0.308)
-29.812 (-1.010)
• Sales Growth Rate (SL) (Control)
0.025* (-1.765)
-0.219 (-0.909)
0.086 (0.396)
-0.009 (-1.695)
R2 0.588 0.359 0.362 0.629
Adjusted R2 0.408 0.078 0.083 0.467
F-Ratio 3.266** 1.278 1.297 3.876**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables The results of multiple regression analysis for the variables influencing the profitability ratio of Breweries
industries in Nigeria were summarized in Table 4.3.5 above. Out of the four functional models of the
multiple regression calculated, the Exponential Regression model was chosen because it has the highest
number of significant variables as well as a very significant F-ratio (3.876***) value which indicated that
the choice model suited the analysis. Furthermore, the results of the analysis revealed an R2 value of 0.629
thus indicating that 62.9% variation in the profitability ratio (dependent) variable of Breweries Industries in
Nigeria was accounted for by the explanatory (independent variables) considered in the analysis.
Specifically the results showed that AR, STO and LQ had significant positive relationships with the
98
industries’ profitability ratio at 1% level of significance. This implies that a unit increases in values of AR,
STO and LQ shall bring about corresponding increases in the profitability ratio of Breweries industries in
Nigeria. On the other hand, the industries’ AP and CCC had negative and non-significant relationship with
the profitability ratio at 1% levels of significance. This means that unit increase in the variable shall bring
about corresponding decrease in the profitability ratio of Breweries industries in Nigeria.
4.3.6: Packaging Firms Table 4.3.6: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of Packaging firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.375*** (3.230)
-0.045 (-0.289)
-0.504 (-1.236)
-0.608 (-1.540)
Accounts Receivable Ratio (AR)
-0.751** (-2.794)
-0.155 (-1.217)
-0.236 (-0.706)
-2.187** (-2.393)
Stock Turnover Ratio (STO)
-0.303 (-1.274)
0.079 (0.880)
0.457* (1.928)
-0.863 (-1.066)
Accounts Payable Ratio (AP)
0.073 (0.721)
0.012 (0.174)
0.395** (2.136)
0.916** (2.662)
Cash Conversion Cycle Ratio (CCC)
0.356** (2.141)
-0.149* (-2.102)
-0.312 (-1.667)
0.752 (1.331)
Liquidity Ratio (LQ) 0.014 (0.867)
0.189 (1.452)
0.027 (0.078)
0.039 (0.494)
Debt Ratio (DT) (Control)
0.160 (0.963)
-0.012 (-0.347)
0.277*** (2.986)
0.303 (0.536)
Sales Growth Rate (SL) (Control)
3.054E-7 (.355)
-0.009 (-0.336)
-0.014 (-0.205)
-5.342E-7 (-0.183)
R2 0.372 0.339 0.649 0.443 Adjusted R2 0.097 0.050 0.495 0.200 F-Ratio 1.354 1.174 4.220*** 1.822
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are t-values while those outside brackets are coefficients of the variables 4. DT and SL are not considered in the interpretation because they are controls variables The results of multiple regression analyses for the variables influencing the profitability ratio of Packaging
industries in Nigeria were summarized in Table 4.3.6. Out of the four functional models of the multiple
regression calculated, the Double Log Regression model was chosen because it has the highest number of
significant variables as well as a very significant F-ratio (4.220***) value which indicated that the choice
model best suited the analysis. Furthermore, the results of the analysis revealed an R2 value of 0.649 thus
indicating that 64.9% variation in the profitability ratio (dependent variable) of Breweries Industries in
99
Nigeria was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that AR, andLCCC had significant negative relationships with the industries’
profitability ratio at 1%, 5% and 10% levels of significance. This implies that a unit increase in values of
AR, and CCC shall bring about corresponding decrease in the profitability ratio of Packaging industries in
Nigeria. On the other hand, the industries’ STO,AP, and LQ had significant and positive relationship with
the profitability ratio at 1% levels of significance. This means that unit increase in the variable shall bring
about corresponding increase in the profitability ratio of Packaging industries in Nigeria.
4.3.7: Automobile and Tyre firms Table 4.3.7: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of Automobile and Tyre firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.287 (1.486)
-0.001 (-0.006)
0.497 (0.399)
-1.022 (-1.244)
Accounts Receivable Ratio (AR)
0.466 (1.022)
-0.070 (-0.271)
0.510 (0.425)
0.723 (0.373)
Stock Turnove Ratior (STO)
-0.289 (-0.496)
-0.707 (-1.369)
0.628 (0.261)
0.582 (0.235)
Accounts Payable Ratio (AP)
0.117 (0.212)
0.013 (0.022)
1.061 (0.393)
0.618 (0.262)
Cash Conversion Cycle Ratio (CCC)
-0.255 (-0.671)
0.356 (1.149)
-0.423 (-0.293)
-0.264 (-0.163)
Liquidity Ratio (LQ) -0.080 (-1.187)
-0.245 (-1.172)
-0.395 (-0.405)
-0.140 (-0.486)
Debt Ratio Rate (DT) (Control)
-4.132 (-1.254)
0.290 (0.853)
1.631 (1.029)
-12.527 (0.892)
Sales Growth (SL) (Control)
0.000 (-0.777)
-0.060 (-0.445)
-0.127 (-0.201)
0.000 (-0.032)
R2 0.594 0.664 0.338 0.334
Adjusted R2 -0.116 0.075 -0.820 -0.832
F-Ratio 0.837 1.127 0.292 0.286
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
100
The results of multiple regression analysis for the variable influencing the profitability ratio of automobile
and tyre industries in Nigeria were summarized in Table 4.3.7 above. From the results presented above and
out of the four functional models of the multiple regression calculated, the Semi log regression model was
chosen because it has the highest number of significant variables as well as a very significant F-ratio
(1.127***) value which indicated that the choice model fitted the analysis. Furthermore, the results of the
analysis revealed an R2 value of 0.664 thus indicating that 66.4% variation in the profitability ratio
(dependent variable) of Automobile and Tyre firms in Nigeria was accounted for by the explanatory
(independent) variables considered in the analysis. Specifically the results showed that CCC had significant
positive relationships with the industries’ profitability ratio at 1% level of significance. This implies that a
unit increase in CCC shall bring about corresponding increase in the profitability ratio of automobile and
Tyre industries in Nigeria. On the other hand, the industries’ STO and LQ all had significant but negative
relationships with the profitability ratio at 1% levels of significance. This also means that unit increases in
the variables shall bring about corresponding decreases in the profitability ratio of the industries in
Nigeria.AR had significant negative relationships with the profitability of the firms under study, while AP
had significant relationship with the profitability ratio at 1% levels of significance.
101
4.3.8: ALL MANUFACTURING FIRMS IN NIGERIA Table 4.3.8: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of all Manufacturing firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 1.137 (3.399)
1.004 (0.214)
-0.539*** (-5.799)
-0.770*** (-12.957)
Accounts Receivable
Ratio [AR]
0.000 (0.007)
1.480 (0.423)
0.843*** (0.622)
0.001 (1.601)
Stock Turnover Ratio (STO)
-1.561 (-0.806)
-7.493 (-1.427)
-0.730*** (-0.589)
-0.031 (-0.796)
Accounts Payable Ratio (AP)
1.081 (0.591)
2.993 (0.675)
-0.416*** (-2.457)
0.066* (1.800)
Cash Conversion Cycle Ratio (CCC)
-0.401 (-0.341)
1.939 (.554)
0.015*** (0.013)
-0.022 (-0.946)
Liquidity Ratio (LQ) -0.869 (-0.638)
-14.860** (-2.260)
-0.428*** (-3.281)
-0.020 (-0.732)
Debt Ratio (DT) (Control)
-1.694 (-0.215)
-4.655* (-1.838)
-.170*** (-3.404)
0.282* (1.800)
Sales Growth Rate (SL) (Control)
-2.040E-5 (-0.108)
-2.195 (-1.007)
0.036 (0.841)
-5.304E-7 (-0.142)
R2 0.005 0.055 0.123 0.058
Adjusted R2 -0.022 0.029 0.100 0.032
F-Ratio .198 2.117** 5.152*** 2.232**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables The results of multiple regression analysis of pooled data for the variables influencing the profitability ratio
of all manufacturing firms in Nigeria were summarized in Table 4.3.8. Out of the four functional forms of
the multiple regression calculated, the Double Log regression model was chosen because it has the highest
number of significant variables as well as a very significant F-ratio (5.152***) value which indicated that
the choice model suited the analysis. Furthermore, the results of the analysis revealed an R2 value of 0.123
thus indicating that 12.3% variation in the profitability ratio (dependent variable) of all manufacturing firms
in Nigeria was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that STO, AP and LQ had significant negative relationships with the
industries’ profitability ratio at 1% level of significance. This implies that a unit increases in values of STO,
102
AP and LQ shall bring about corresponding decrease in the profitability ratio of all manufacturing firms in
Nigeria. On the other hand, the industries’ AR had significant positive relationships with the profitability
ratio at 1% levels of significance. This means that unit increases in the variables shall bring about
corresponding increase in the profitability ratio of all manufacturing firms in Nigeria. Results also showed
that the firms’ CCC has positive but non- significant relationship with their profitability ratio.
4.4: Discussion of individual Industries’ Results
This section of the study discusses the individual industries’ results in order to find out the robustness of the results. Table 4.4.1.: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Seven Up Bottling Company
Variables
Linear
Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.586
(1.257)
-0.114
(-0.187)
-2.606
(-0.688)
-0.471
(-0.094)
Accounts Receivable Ratio
(AR)
1.519
(0.552)
-0.389
(-0.590)
-1.236
(-0.300)
-6.846
(-0.231)
Stock Turnover Ratio(STO)
-1.855
(-1.463)
0.758
(1.201)
0.734
(0.186)
3.377
(0.276)
Accounts Payable Ratio (AP)
-0.223
(-1.534)
-0.106
(-0.993)
-0.294
(-0.441)
1.377
(0.880)
Cash Conversion Cycle Ratio (CCC)
-0.058
(-0.346)
-0.056
(-0.277)
-0.057
(-0.046)
1.253
(0.691)
Liquidity Ratio (LQ) 0.097
(0.439)
0.763
(1.030)
2.531
(0.547)
-1.289
(-0.539)
Debt Ratio (DT) (Control)
-1.267*
(-2.262)
-0.097
(-1.235)
-0.241
(-0.494)
2.555
(0.424)
103
Continued from table 4.4.1:
Sales Growth Rate (SL) (Control)
5.403E-6
(1.863)
0.029
(1.154)
0.119
(0.764)
-2.112E-5
(-0.677)
R2 0.774 0.554 0.275 0.257
Adjusted R2 0.297 -0.225 -0.993 -1.042
F-Ratio 1.663 0.711 0.217 0.198
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables The results of multiple regression analysis for the variable influencing the profitability ratio of Seven UP
Nigeria PLC were summarized in Table 4.4.1. above. From the results presented above and out of the four
functional models of the multiple regression calculated, the Linear Regression model was chosen because it
has the highest number of significant variables as well as a very significant F-ratio (1.663***) value which
indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2
value of 0.774 thus indicating that 77.4% variation in the profitability ratio (dependent variable) of Seven
UP Nigeria PLC was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that STO and AP had significant negative relationships with the company’s
profitability ratio at 1% level of significance. This implies that a unit increase in STO and AP shall bring
about corresponding decrease in the profitability ratio of Seven UP Nigeria PLC. On the other hand, the
company’s AR had significant positive relationships with the profitability ratio at 5% and 1% levels of
significance respectively. This means that unit increases in the variable shall bring about corresponding
increase in the profitability ratio of the company in Nigeria. The LQ of the company had positive but non-
significant relationship with the profitability ratio of the company, while CCC had negative and non-
significant relationship with the companies profitability.
Table 4.4..2: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Cadbury Nigerian PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.087 (0.675)
-0.068 (-0.589)
1.065* (2.441)
0.132 (0.116)
Accounts Receivable Ratio (AR)
0.001 (0.004)
-0.039 (-1.023)
3.418*** (4.619)
6.147 (0.831)
104
Continued from table 4.4.2:
Stock Turnover Ratio (STO)
0.177 (0.698)
-0.050 (-0.729)
-2.055** (-3.023)
0.969 (0.195)
Accounts Payable Ratio (AP)
-0.106 (-0.869)
-0.059 (-1.493)
-1.606* (-2.622)
-2.318 (-0.581)
Cash Conversion Cycle Ratio (CCC)
-0.068 (-0.869)
0.013 (0.289)
-0.101 (-0.394)
0.938 (-0.340)
Liquidity Ratio (LQ) -0.002 (-0.016)
-0.142 (-0.509)
-1.507** (-3.376)
-1.040 (-1.813)
Debt Ratio (DT) (Control)
-0.103 (-1.180)
-0.018 (-0.675)
-0.570* (-2.403)
4.413 (0.776)
Sales Growth Rate (SL) (Control)
0.001 (0.459)
0.023 (0.660)
-1.291*** (-5.273)
-0.010 (-1.161)
R2 0.628 0.722 0.952 0.562 Adjusted R2 -0.022 0.234 0.868 -205 F-Ratio 0.966 1.481 11.373** 0.733
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Cadbury
Nigerian PLC in Nigeria were summarized in Table 4.4.2 above. From the results presented above and out
of the four functional models of the multiple regression calculated, the double log Regression model was
chosen because it has the highest number of significant variables as well as a very significant F-ratio
(11.373***) value which indicated that the choice model fitted the analysis. Furthermore, the results of the
analysis revealed an R2 value of 0.952 thus indicating that 95.2% variation in the profitability ratio
(dependent variable) of Cadbury Nigerian PLC was accounted for by the explanatory (independent)
variables considered in the analysis. Specifically the results showed that STO, AP and LQ had significant
negative relationships with the industries’ profitability ratio at 1% level of significance. This implies that a
unit increase in the variables shall bring about corresponding decrease in the profitability ratio of Cadbury
Nigerian PLC. On the other hand, the company’s AR had significant and positive relationships with the
profitability ratio at 1% levels of significance. This means that unit increases in the variable shall bring
about corresponding increase in the profitability ratio of the company in Nigeria. However, CCC had
negative but non-significant relationship with the company’s profitability ratio.
105
Table 4.4.3: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Flour mills Nigerian PLC
Variables
Linear
Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.169
(-1.414)
-0.089
(-0.429)
-0.212
(-0.449)
-0.829
(-1.060)
Accounts Receivable Ratio
(AR)
-1.738*
(-2.230)
-0.129
(-0.367)
0.149
(0.962)
1.175
(1.261)
Stock Turnover Ratio(STO)
-0.070
(-0.133)
0.138
(0.430)
0.961**
(3.442)
0.702
(0.459)
Accounts Payable Ratio (AP)
0.454
(1.079)
0.079
(0.272)
0.219
(1.369)
-1.197
(-1.621)
Cash Conversion Cycle Ratio (CCC)
0.301
(1.036)
0.018
(0.146)
-0.159
(-0.888)
-1.107
(-1.266)
Liquidity Ratio (LQ) 0.307***
(5.080)
0.365
(1.724)
-1.171
(-1.026)
0.046
(0.069)
Debt Ratio (DT) (Control)
-0.847
(-1.414)
-0.100
(-0.892)
-0.154
(-1.420)
1.355*
(2.562)
Sales Grow Rateth (SL) (Control)
0.003**
(2.939)
0.152
(1.312)
0.082
(0.565)
-0.013
(-1.475)
R2 0.916 0.839 0.953 0.857
Adjusted R2 0.770 0.557 0.869 0.608
F-Ratio 6.269 2.975 11.467** 3.436
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Flourmills
Nigerian PLC were summarized in Table 4.4.3 above. From the results presented above and out of the four
106
functional models of the multiple regression calculated, the double log regression model was chosen because
it has the highest number of significant variables as well as a very significant F-ratio (11.467***) value
which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an
R2 value of 0.953 thus indicating that 95.3% variation in the profitability ratio (dependent variable) of
Flourmills Nigerian PLC was accounted for by the explanatory (independent) variables considered in the
analysis. Specifically the results showed that AR, STO, and AP had significant negative relationships with
the company’s profitability ratio at 1% level of significance. This implies that a unit increases in the
variables shall bring about corresponding decrease in the profitability ratio of Flourmills Nigerian PLC. On
the other hand, the company’s CCC, and LQ also had significant positive relationship with the profitability
ratio at 1% levels of significance. This means that unit increases in the variables shall bring about
corresponding increases in the profitability ratio of the company in Nigeria.
107
Table 4.4.4: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Nestle Foods Nigeria PLC
Variables
Linear
Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.310
(-0.494)
0.387
(1.772)
-0.179
(-0.176)
-0.113
(-1.039)
Accounts Receivable Ratio
(AR)
-0.547
(-0.105)
0.080
(0.929)
0.415
(1.040)
3.095
(0.129)
Stock Turnover Ratio(STO)
1.388
(1.444)
0.278
(1.899)
0.939
(1.378)
-2.209
(-0.502)
Accounts Payable Ratio (AP)
-0.017
(-0.006)
-0.105
(-1.101)
-0.200
(-0.452)
4.819
(0.379)
Cash Conversion Cycle Ratio (CCC)
-0.158
(-0.288)
-0.090
(-1.608)
-0.190
(-0.732)
1.616
(0.646)
Liquidity Ratio (LQ) 0.316
(1.251)
0.032
(0.131)
-1.038
(-0.908)
-0.490
(-0.424)
Debt Ratio (DT) (Control)
-0.078
(-0.056)
0.122***
(4.574)
0.342*
(2.759)
-2.130
(-0.332)
Sales Growth Ratio (SL) (Control)
0.003
(0.629)
0.144
(1.673)
0.207
(0.517)
-0.021
(-0.863)
R2 0.497 0.984 0.943 0.622
Adjusted R2 0.817 0.956 0.843 -0.040
F-Ratio 2.556 34.898*** 9.420** 0.939
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Nestle food
PLC were summarized in Table 4.4.4 above. From the results presented above and out of the four functional
108
models of the multiple regression calculated, the semi log Regression model was chosen because it has the
highest number of significant variables as well as a very significant F-ratio 34.898***) value which
indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2
value of 0.984 thus indicating that 98.4% variation in the profitability ratio (dependent variable) of Nestle
food PLC was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that AP had significant negative relationships with the company’s profitity
ratio at 1% level of significance. This implies that a unit increases in the variable shall bring about
corresponding decreases in the profitability ratio of Nestle food PLC. On the other hand, the company’s
STO had significant positive relationships with the profitability ratio at 1% levels of significance. This
means that unit increases in the variables shall bring about corresponding increases in the profitability ratio
of the manufacturing industries in Nigeria, while AR and LQ had positive but non-significant relationship.
CCC had negative and non-significant relationship, with the industries profitability.
Table 4.4.5: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Nigeria Bottling Company
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.111* (-2.593)
0.344 (1.520)
-3.464*** (-10.395)
-1.221 (-0.909)
Accounts Receivable Ratio (AR)
0.269*** (5.321)
0.003 (0.042)
0.029 (0.283)
0.587 (0.371)
Stock Turnover Ratio (STO)
0.154 (0.995)
0.298 (0.643)
-4.023*** (-6.064)
4.248 (0.880)
Accounts Payable Ratio (AP)
-0.311*** (-4.778)
-0.029 (-0.268)
0.601** (3.606)
0.036 (0.018)
Cash Conversion Cycle Ratio (CCC)
-0.184* (-2.613)
-0.029 (-0.450)
-0.190 (-1.987)
-0.248 (-0.113)
Liquidity (LQ) 0.290*** (10.776)
0.614 (1.683)
0.758 (1.559)
-0.975 (-1.158)
Debt Ratio (DT) (Control)
-0.058 (-1.733)
0.008 (0.171)
-0.283*** (-4.849)
-0.179 (-0.170)
Sales Growth Rate (SL) (Control)
8.800E-6 (1.462)
-0.013 (-0.253)
0.028 (0.384)
0.000 (1.074)
R2 0.994 0.726 0.960 0.688 Adjusted R2 0.983 0.245 0.890 0.142 F-Ratio 89.286*** 1.511 13.705** 1.260
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + U i 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
109
The results of multiple regression analysis for the variable influencing the profitability ratio of Nigeria
bottling company were summarized in Table 4.4.5 above. From the results presented above and out of the
four functional models of the multiple regression calculated, the Linear Regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (89.286***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an adjusted R2 value of 0...983 thus indicating that 98.3% variation in the profitability ratio
(dependent variable) of Nigeria bottling company was accounted for by the explanatory (independent)
variables considered in the analysis. Specifically the results showed that AR, STO and LQ had significant
positive relationships with the industries’ profitability ratio at 1% level of significance. This implies that a
unit increase in the variables shall bring about corresponding increase in the profitability ratio of Nigeria
bottling company. On the other hand, the company’s AP and CCC both had significant but negative
relationships with the profitability ratio at 1% levels of significance. This means that unit increases in the
variables shall bring about corresponding decreases in the profitability ratio of the industries in Nigeria.
Table 4.4.6: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of First Aluminum Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.476 (-1.754)
-0.232** (-3.844)
-1.517** (-2.789)
0.192 (0.037)
Accounts Receivable Ratio (AR)
0.057 (0754)
-0.007 (-0.167)
-1.319*** (-5.191)
-0.222 (-0.153)
Stock Turnover Ratio (STO)
-0.511 (-1.908)
-0.241 (-1.360)
2.416 (1.617)
1.393 (0.268)
Accounts Payable Ratio (AP)
0.225** (3.249)
0.081 (0.819)
-1.264 (-1.985)
-1.629 (-1.212)
Cash Conversion Cycle Ratio (CCC)
-0.043 (-0.742)
0.051 (2.045)
0.399 (1.941)
0.047 (0.042)
Liquidit Ratioy (LQ) 0.560 (1.761)
-0.350* (-2.734)
-15.097** (-4.411)
-0.428 (-0.070)
Debt Ratio (DT) (Control)
-0.146 (-0.840)
-0.046 (-0.371)
0.315 (1.651)
2.961 (0.877)
Sales Growth Rate (SL) (Control)
0.000 (1.985)
0.073 (0.815)
0.223 (1.544)
0.000 (-0.096)
R2 0.889 0.896 0.959 0.620 Adjusted R2 0.694 0.714 0.887 -0.044 F-Ratio 4.565* 4.929* 13.281** 0.934
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
110
The results of multiple regression analysis for the variable influencing the profitability ratio of First
Aluminium PLC were summarized in Table 4.4.6 above. From the results presented above and out of the
four functional models of the multiple regression calculated, the Double log Regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio 13.281***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.959 thus indicating that 95.9% variation in the profitability ratio (dependent
variable) of First Aluminum PLC was accounted for by the explanatory (independent) variables considered
in the analysis. Specifically the results showed that STO and CCC had significant positive relationships with
the industries’ profitability ratio at 1% level of significance. This implies that a unit increase in the variables
shall bring about corresponding increase in the profitability ratio of First Aluminum PLC. On the other hand,
the industries’ AR, AP and LQ all had significant positive relationships with the profitability ratio at 1%
levels of significance. This means that unit increases in the variables shall bring about corresponding
decreases in the profitability ratio of the industries in Nigeria.
Table 4.4.7: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Aluminum Extrusion Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.262*** (4.788)
-0.232** (-3.844)
-0.036 (-0.049)
-1.465*** (-5.495)
Accounts Receivable Ratio (AR)
0.198 (1.557)
0.081 (0.819)
0.639 (1.175)
-0.829 (-1.334)
Stock Turnover Ratio (STO)
-0.459* (-2.482)
-0.241 (-1.360)
-1.645 (-0.763)
0.931 (1.033)
Accounts Payable Ratio (AP)
0.070 (0.603)
0.081 (0.819)
1.010 (0.842)
0.785 (1.383)
Cash Conversion Cycle Ratio (CCC)
-0.188 (-1.588)
0.051s (2.045)
-0.519 (-1.718)
0.797 (1.357)
Liquidity Ratio (LQ) -0.244 (-2.047)
-0.350* (-2.734)
1.771 (1.137)
0.788 (1.355)
Debt Ratio (DT) (Control)
-0.032 (-0.207)
-0.046 (-0.371)
-1.571 (-1.035)
1.114 (1.458)
Sales Growth Rate (SL) (Control)
0.001 (1.389)
0.059 (0.815)
-1.325 (-1.495)
-0.002 (-1.058)
R2 0.922 0.896 0.593 0.929 Adjusted R2 0.786 0.714 -0.120 0.805 F-Ratio 6.774* 4.929* 0.832 7.482**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Aluminium
Extrusion PLC were summarized in Table 4.4.7 above. From the results presented above and out of the four
111
functional models of the multiple regression calculated, the Exponetial regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (7.482***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.929 thus indicating that 92.9% variation in the profitability ratio (dependent
variable) of Aluminum Extrusion PLC was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that AR had significant negative relationship with
the company’s profitability ratio at 1% level of significance. This implies that unit increases in the variable
shall bring about corresponding decrease in the profitability ratio of Aluminum Extrusion PLC. On the other
hand, the industries’ AP, STO, LQ and CCC all had significant and positive relationships with the
profitability ratio at 1% levels of significance. This means that unit increases in the variables shall bring
about corresponding increases in the profitability ratio of the industries in Nigeria.
112
B.O.C CASE Table 4.4.8: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of B.O.C Case Nigeria PLC
Variables
Linear
Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.551
(-1.668)
0.351*
(2.382)
-0.638**
(-2.914)
-1.335
(-0.435)
Accounts Receivable Ratio
(AR)
0.001***
(12.202)
0.131**
(2.886)
0.122
(1.800)
4.383E-5
(0.088)
Stock Turnover Ratio (STO)
-0.054*
(-0.251)
-0.733
(-2.000)
0.438*
(2.730)
1.054
(0.523)
Accounts Payable Ratio (AP)
0.373***
(11.388)
0.882***
(8.171)
0.438*
(2.730)
0.051
(0.168)
Cash Conversion Cycle Ratio (CCC)
-0.289
(-1.067)
0.299**
(2.771)
0.135
(0.841)
0.287
(0.114)
Liquidity Ratio (LQ) -0.405*
(-2.367)
1.683**
(3.712)
1.073
(1.594)
-0.063
(-0.039)
Debt Ratio (DT) (Control)
-9.821**
(-3.064)
0.176*
(2.271)
0.195
(1.702)
14.032
(0.471)
Sales Growth Rate (SL) (Control)
0.000
(1.287)
0.102
(1.377)
0.073
(0.664)
0.000
(0.189)
R2 0.995 0.985 0.924 0.294
Adjusted R2 0.987 0.959 0.790 -0.941
F-Ratio 118.302*** 37.311*** 6.921** 0.238
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
113
The results of multiple regression analysis for the variable influencing the profitability ratio of B.O.Case
Nigeria PLC were summarized in Table 4.4.8 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the Linear regression model was chosen because it
has the highest number of significant variables as well as a very significant F-ratio (118.302***) value
which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an
R2 value of 0.995 thus indicating that 99.5% variation in the profitability ratio (dependent variable) of
B.O.Case Nigeria PLC was accounted for by the explanatory (independent) variables considered in the
analysis. Specifically the results showed that LQ, STO and CCC had significant negative relationships with
the industries’ profitability ratio at 1% level of significance. This implies that a unit increase in LQ,STO and
CCC shall bring about corresponding decrease in the profitability ratio of B.O. Case Nigeria PLC. On the
other hand, the industries’ had significant but positive relationship with the profitability ratio at 1% levels of
significance. This means that unit increases in the variables shall bring about corresponding increases in the
profitability ratio of the industries in Nigeria, while AR had positive but non-significant relationship with the
industries profitability.
ENAMELWARE Table 4.4.9: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Enamelware Nigerian PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.115** (2.629)
-0.022 (-0.415)
-1.980*** (-6.217)
-0.389 (-0.533)
Accounts Receivable Ratio (AR)
0.023 (0.633)
-0.002 (-0.163)
0.137 (0.165)
0.464 (0.757)
Stock Turnover Ratio (STO)
-0.121 (-0.627)
-0.077 (-0.934)
-0.689 (-1.375)
-2.072 (-0.641)
Accounts Payable Ratio (AP)
-0.088** (-2.667)
-0.037* (-2.480)
-0.251** (-2.760)
0.480 (0.876)
Cash Conversion Cycle Ratio (CCC)
-0.030 (-0.811)
0.001 (0.121)
-0.010 (-0.152)
-0.313 (-0.512)
Liquidity Ratio (LQ) -0.000 (-0.319)
0.002 (0.063)
0.234 (0.215)
0.023 (0.575)
Debt Ratio Rate (DT) (Control)
NA NA NA
NA
Sales Growth (SL) (Control)
-2.177E-7 (-0.008)
0.463 (0.004)
0.039 (0.819)
0.000 (0.772)
R2 0.704 0.693 0.765 0.350 Adjusted R2 0.349 0.324 0.482 -0.429 F-Ratio 1.983 1.880 2.709 0.450
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
114
The results of multiple regression analysis for the variable influencing the profitability ratio of Enamelware
Nigeria PLC were summarized in Table 4.4.9 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the Double log regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (2.709***
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.765 thus indicating that 76.5% variation in the profitability ratio (dependent
variable) of Enamelware Nigeria PLC was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that AR and LQ had significant positive
relationships with the company’s profitability ratio at 1% level of significance. This implies that a unit
increase in LQ shall bring about corresponding increase in the profitability ratio of Enamelware Nigeria
PLC. On the other hand, the company’s AR and STO all had significant but negative relationships with the
profitability ratio at 1% levels of significance. This means that unit increases in the variables shall bring
about corresponding decreases in the profitability ratio of the industries in Nigeria.And again CCC had
negative and non-significant relationship with the industries profitability.
115
VTA FOAM Table 4.4.10: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Vita Foam PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.963 (1.808)
2.663 (1.606)
0.658 (1.328)
2.457* (2.225)
Accounts Receivable Ratio (AR)
0.227 (0.830)
-1.952* (-2.253)
-0.294 (-1.278)
-3.659*** (-6.449)
Stock Turnover Ratio (STO)
-0.183 (-0.684)
0.328 (0.398)
0.062 (0.251)
-0.899 (-3.554)
Accounts Payable Ratio (AP)
-0.102 (-0.384)
-0.495 (-0.270)
-0.251** (-2.760)
0.251 (0.455)
Cash Conversion Cycle (CCC)
-0.169 (-0.566)
-0.256 (-0.912)
-0.010 (-0.152)
0.894** (3.527)
Liquidity Ratio (LQ) -0.646 (-1.807)
-20.974* (-2.745)
-0.108** (-3.550)
-1.810* (-2.441)
Debt Ratio (DT) (Control)
1.507*** (32.220)
0.0650 (1.461)
0.384 0.133
0.051 0.527
Sales Growth Rate (SL) (Control)
0.000 (-0.950)
-0.677 (-1.813)
-0.084 (-0.753)
0.000 (-1.896)
R2 0.998 0.867 0.922 0.951 Adjusted R2 0.995 0.633 0.786 0.886 F-Ratio 347.90 3.712 6.768** 11.190**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Vita foam
PLC were summarized in Table 4.4.10 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the Linear Regression model was chosen because it
has the highest number of significant variables as well as a very significant F-ratio (347.90***) value which
indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2
value of 0.998 thus indicating that 99.3% variation in the profitability ratio (dependent variable) of Vita
foam PLC was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that AR had significant positive relationship with the company’s profitability
ratio at 1% level of significance. This implies that a unit increase in the variable shall bring about
corresponding increase in the profitability ratio of Vita foam PLC. On the other hand, the company’s
STO,AP, CCC and LQ all had significant but negative relationships with the profitability ratio at 1% levels
of significance. This means that unit increases in the variables shall bring about corresponding decreases in
the profitability ratio of the company in Nigeria.
116
Vono products
Table 4.4.11: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Vono Product Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant -0.166 (-1.894)
-0.080 (-0.662)
-0.413 (-0.737)
0.014 (0.020)
Accounts Receivable Ratio (AR)
-0.488** (-3.621)
-0.351 (-1.847)
1.417 (1.610)
1.385 (1.224)
Stock Turnover Ratio (STO)
0.068 (0.537)
0.205 (1.276)
0.060 (0.081)
0.450 (0.422)
Accounts Payable Ratio (AP)
0.062 (0.912)
0.016 (0.063)
-1.005 (-0.875)
0.358 (0.629)
Cash Conversion Cycle Ratio (CCC)
0.049 (0.543)
0.075 (0.523)
-0.954 (-1.446)
0.383 (0.508)
Liquidity Ratio (LQ) 0.199 (1.791)
0.382 (1.532)
-3.584** (-3.089)
-1.405 (-1.507)
Debt Ratio (DT) (Control)
0.257 (0.376)
0.082 (0.351)
-0.614 -0.572
1.202 0.209
Sales Growth Rate (SL) (Control)
-0.001 (-4.402)
-0.124 (-2.011)
0.064 (0.226)
0.001 (0.450)
R2 0.924 0.769 0.789 0.620 Adjusted R2 0.791 0.365 0.420 -0.045 F-Ratio 6.938** 1.902 2.140 0.932
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Vono
products Nigeria PLC were summarized in Table 4.4.11 above. From the results presented above and out of
the four functional models of the multiple regression calculated, the Linear Regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (6.938***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.924 thus indicating that 92.4% variation in the profitability ratio (dependent
variable) of Vono products Nigeria PLC was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that AR had significant negative relationship with
the company’s profitability ratio at 1% level of significance. This implies that a unit increase in the variables
shall bring about corresponding decrease in the profitability ratio of Vono products Nigeria PLC. On the
other hand, the company’s LQ all had significant but positive relationships with the profitability ratio at 10%
117
levels of significance. This means that unit increase in the variables shall bring about corresponding increase
in the profitability ratio of the company’s in Nigeria. However, AP, STO and CCC had positive but non
significant relationship with profitability ratio of the company
Evans medical Table 4.4.12: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Evans Medical, Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.235 (0.974)
-0.203 (-1.168)
0.256 (0.154)
0.133 (0.059)
Accounts Receivable Ratio (AR)
-0.097
(-0.202)
-0.046
(-0.422)
0.314
(0.297)
1.587
(0.351)
Stock Turnover Ratio (STO)
-0.134
(-0.497)
-0.525
(-1.541)
3.414
(1.044)
1.279
(0.505)
Accounts Payable Ratio (AP)
-0.645
(-1.316)
-0.106
(-0.455)
-0.136
(-0.061)
-0.565
(-0.123)
Cash Conversion Cycle Ratio (CCC)
-0.128
(-1.109)
-0.047
(-0.190)
-0.475
(-0.202)
0.255
(0.233)
Liquidity Ratio (LQ) 0.138 (0.860)
0.553 (1.256)
-1.192 (-0.282)
-1.820 (-1.206)
Debt Ratio (DT) (Control)
NA NA
NA
NA
Sales Growth Rate (SL) (Control)
0.000 (-0.206)
0.003 (0.056)
-0.157 (-0.338)
0.006 (0.458)
R2 0.639 0.590 0.270 0.395
Adjusted R2 0.206 0.097 -0.607 -0.330
F-Ratio 1.475 1.197 0.308 0.545
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Evans
medical Nigeria PLC were summarized in Table 4.4.12 above. From the results presented above and out of
the four functional models of the multiple regression calculated, the Exponential Regression model was
chosen because it has the highest number of significant variables as well as a very significant F-ratio
118
(0.545***) value which indicated that the choice model fitted the analysis. Furthermore, the results of the
analysis revealed an R2 value of 0.395 thus indicating that 39.5% variation in the profitability ratio
(dependent variable) of Evans medical Nigeria PLC was accounted for by the explanatory (independent)
variables considered in the analysis. Specifically the results showed that AR, CCC and STO had significant
positive relationships with the company’s profitability ratio at 1% level of significance. This implies that a
unit increase in the variables shall bring about corresponding increase in the profitability ratio of Evans
medical Nigeria PLC. On the other hand, the company’s AP and LQ all had significant but negative
relationships with the profitability ratio at 1% levels of significance. This means that unit increases in the
variables shall bring about corresponding decreases in its profitability ratio. The LQ of the company had
positive but non-significant relationship with the profitability ratio.
MAY BAKER Table 4.4.13: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of May Baker Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.043 (0.533)
0.168 (1.947)
-1.555 (-0.648)
1.480** (3.062)
Accounts Receivable Ratio (AR)
0.126 (0.275)
0.256 (1.315)
0.726 (0.134)
-1.678 (-0.612)
Stock Turnover Ratio (STO)
0.051 (0.144)
0.112 (0.326)
1.840 (0.192)
-3.642 (-1.739)
Accounts Payable Ratio (AP)
0.109 (0.828)
-0.094 (-0.929)
-1.940 (-0.686)
-0.431 (-0.550)
Cash Conversion Cycle Ratio (CCC)
0.101 (0.913)
-0.176 (-1.704)
-2.721 (-0.944)
-1.145 (-1.742)
Liquidity Ratio (LQ) -0.045 (-0.546)
-0.176 (-0.727)
-1.829 (-0.361)
0.316 (0.647)
Debt Ratio (DT) (Control)
0.198 (0.722)
-0.029 -0.732
0.153 (0.139)
-3.218 -1.966
Sales Growth Ratio (SL) (Control)
0.001 (0.873)
0.028 (1.001)
0.033 (0.044)
-0.012 (-1.881)
R2 0.600 0.743 0.295 0.906 Adjusted R2 -0.101 0.294 -0.938 0.740 F-Ratio 0.856 1.655 0.239 5.478*
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
119
The results of multiple regression analysis for the variable influencing the profitability ratio of May Baker
Medicals Nigeria were summarized in Table 4.4.13 above. From the results presented above and out of the
four functional models of the multiple regression calculated, the Exponential regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (5.478***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.906 thus indicating that 90.6% variation in the profitability ratio (dependent
variable) of May Baker Medicals Nigeria was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that LQ had significant positive relationship with
the company’s profitability ratio at 5% level of significance. This implies that a unit increase in STO shall
bring about corresponding increase in the profitability ratio of May Baker Medicals Nigeria. On the other
hand, the company’s AR, STO, AP, and CCC all had significant but negative relationships with the
profitability ratio at 1% and 5% levels of significance respectively. This means that unit increases in the
variables shall bring about corresponding decreases in the profitability ratio of the industry in Nigeria.
PHARMA- DEKO Table 4.4.14: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Pharma-Deko Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.048 (0.097)
-0.053 (0.491)
-0.702 (-1.221)
0.993 (1.085)
Accounts Receivable Ratio (AR)
-0.008 (-4.462)
0.082 (1.449)
-0.089 (-0.297)
0.048 (1.624)
Stock Turnover Rtio (STO)
0.121 (0.514)
0.130 (1.439)
-0.476 (-0.995)
-0.184 (-0.196)
Accounts Payable Ratio (AP)
-0.060 (0.204)
-0.352 (-1.141)
0.476 (0.995)
0.605 (1.121)
Cash Conversion Cycle Ratio (CCC)
0.108 (0.628)
-0.243 (-0.640)
2.157 (-1.078)
0.066 (.006)
Liquidity Ratio (LQ) 0.068 (0.199)
-0.093 (-0.411)
-0.514 (-0.430)
-1.634* (-2.609)
Debt Ratio (DT) (Control)
-0.284 (-0.278)
0.364 0.933
-0.623 (-0.303)
-0.973 -0.520
Sales Growth Rate (SL) (Control)
1.171E-5 (0.030)
0.137** (3.638)
-0.241 (-1.214)
-0.001 (1.152)
R2 0.715 0.896 0.746 0.917 Adjusted R2 0.216 0.714 0.301 0.771 F-Ratio 1.433 4.920* 1.677 6.304**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
120
The results of multiple regression analysis for the variable influencing the profitability ratio of Pharma Deko
Medicals Nigeria PLC were summarized in Table 4.4.14 above. From the results presented above and out of
the four functional models of the multiple regression calculated, the Exponential regression model was
chosen because it has the highest number of significant variables as well as a very significant F-ratio
(6.304***) value which indicated that the choice model fitted the analysis. Furthermore, the results of the
analysis revealed an R2 value of 0.917 thus indicating that 91.7% variation in the profitability ratio
(dependent variable) of Pharma Deko Medicals Nigeria PLC was accounted for by the explanatory
(independent) variables considered in the analysis. Specifically the results showed that AP had significant
positive relationships with the industries’ profitability ratio at 1% level of significance. This implies that a
unit increase in AP shall bring about corresponding increase in the profitability ratio of Pharma Deko
Medicals Nigeria PLC. On the other hand, the company’s STO and LQ both had significant but negative
relationships at 1% and AR at 10% levels of significance with the profitability ratio. This means that unit
increases in the variables shall bring about corresponding decreases in the profitability ratio of the company
in Nigeria. The CCC has positive but non significant relationship with profitability.
BENUE CEMENT Table 4.4.15: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Benue Cement Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 174.06 (0.782)
-353.09 (-1.014)
-1.734 (-0.812)
-0.865 (-1.421)
Accounts Receivable Ratio (AR)
-507.586 (-0.637)
-593.13 (-1.163)
-3.549 (-1.135)
2.104 (0.965)
Stock Turnover Ratio (STO)
-47.919 (-0.409)
133.82 (0.819)
1.304 (1.302)
0.105 (0.327)
Accounts Payable Ratio (AP)
11.254 (0.364)
-241.39 (-0.905)
-2.195 (-1.343)
-0.090 (-1.060)
Cash Conversion Cycle Ratio (CCC)
-24.581 (-0.385)
1150.12 (1.387)
11.111* (2.186)
0.026 (0.148)
Liquidity Ratio (LQ) 678.937 (-0.502)
-256.58 (-0.975)
-1.889 (-1.171)
-2.183 (-0.591)
Debt Ratio (DT) (Control)
-907.937 (-0.682)
-111.28 (-0.814)
-0.467 -0.557
3.066 0.842
Sales Growth Rate (SL) (Control)
-1.267 (-0.524)
-3.408 (-0.036)
-0.415 (-0.717)
0.004 (0.614)
R2 0.217 0.507 0.609 0.428 Adjusted R2 -1.127 -0.355 -0.076 -0.574 F-Ratio 0.159 0.589 0.889 0.427
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
121
The results of multiple regression analysis for the variable influencing the profitability ratio of Benue
cement PLC were summarized in Table 4.4.15 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the double log regression model was chosen because
it has the highest number of significant variables as well as a very significant F-ratio (0.889***) value which
indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2
value of 0.609 thus indicating that 60.9% variation in the profitability ratio (dependent variable) of Benue
cement PLC was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that STO and CCC had significant positive relationships with the industries’
profitability ratio at 1% level of significance. This implies that a unit increase in STO and CCC shall bring
about corresponding increase in the profitability ratio of Benue cement PLC. On the other hand, the
industries’, AP and LQ all had significant but negative relationships with the profitability ratio at 5% and
1% levels of significance. This means that unit increases in the variables shall bring about corresponding
decreases in the profitability ratio of the industries in Nigeria.
Berger paints Table 4.4.16: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Berger Paint PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.095 (1.286)
-0.071 (-0.380)
-0.509 (-0.366)
-0.273 (-0.659)
Accounts Receivable Ratio (AR)
-0.258 (-0.816)
-0.118 (-1.096)
0.199 (0.249)
-0.803 (-0.454)
Stock Turnover Ratio (STO)
0.136 (0.727)
0.171 (0.885)
0.085 (0.059)
0.301 (0.288)
Accounts Payable Ratio (AP)
-0.094 (-0.823)
-0.113 (-1.149)
-0.159 (-0.218)
-0.063* (-2.229)
Cash Conversion Cycle Ratio (CCC)
-0.058 (-0.615)
-0.060 (-0.404)
0.732 (0.667)
-0.063 (-0.118)
Liquidity (LQ) 0.002 (0.065)
0.021 (0.230)
-0.467 (-0.704)
0.186 (1.157)
Debt (DT) (Control) NA NA NA NA Sales Growth Rate (SL) (Control) Ratio
3.114E-5 (0.498)
0.014 (0.594)
0.031 (0.174)
0.000 (-1.344)
R2 0.495 0.502 0.303 0.723 Adjusted R2 -0.111 -0.095 -0.533 0.391 F-Ratio 0.816 0.841 0.363 2.175
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Berger Paints
Nigeria PLC were summarized in Table 4.4.16 above. From the results presented above and out of the four
122
functional models of the multiple regression calculated, the exponential regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (2.175***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.723 thus indicating that 72.3% variation in the profitability ratio (dependent
variable) of Berger Paints Nigeria PLC was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that AR had significant negative relationships
with the company’s profitability ratio at 1% level of significance. This implies that a unit increase in AR
shall bring about corresponding decreases in the profitability ratio of Berger Paints Nigeria PLC. On the
other hand, the company’s LQ had significant but positive relationships with the profitability ratio at 1%
levels of significance. This means that unit increase in the variable shall bring about corresponding increase
in the profitability ratio of the company in Nigeria.
Premier Paints Table 4.4.17: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Premier Paint Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.003 (0.040)
-0.383 (-0.819)
2.389 (0.493)
-1.663 (-0.964)
Accounts Receivable Ratio (AR)
0.523 (0.985)
-0.157 (-0.756)
1.998 (0.930)
-2.031 (-0.171)
Stock Turnover Ratio (STO)
0.050 (0.074)
-0.048 (-0.133)
0.657 (0.175)
-5.163 (-0.340)
Accounts Payable Ratio (AP)
-0.054 (-0.096)
-0.225 (-1.775)
1.257 (0.958)
9.897 (0.783)
Cash Conversion Cycle Ratio (CCC)
0.255 (0.506)
-0.047 (-0.705)
0.031 (0.045)
7.063 (0.629)
Liquidity Ratio (LQ) 0.180 (1.599)
0.264 (1.665)
-1.018 (-0.620)
-1.027 (-0.923)
Deb Raiot (DT) (Control)
-0.086 (-0.376)
0.029 (0.569)
0.200 0.380
0.730 0.143
Sales Growth Rate (SL) (Control)
0.001 (1.462)
0.065 (0.800)
-0.178 (-0.210)
-0.016 (-0.720)
R2 0.968 0.861 0.476 0.475 Adjusted R2 0.911 0.618 -0.440 -0.443 F-Ratio 17.033*** 3.546 0.520 0.517
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
123
The results of multiple regression analysis for the variable influencing the profitability ratio of Premier
paints Nigeria PLC were summarized in Table 4.4.17 above. From the results presented above and out of the
four functional models of the multiple regression calculated, the Linear Regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (17.033***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.968 thus indicating that 96.8% variation in the profitability ratio (dependent
variable) of Premier paints Nigeria PLC was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that AR, LQ and CCC had significant positive
relationships with the industries’ profitability ratio at 5% level of significance. This implies that a unit
increase in AR,LQ and CCC shall bring about corresponding increase in the profitability ratio of Premier
paints Nigeria PLC. On the other hand, the company’s AP and STO all had significant but negative
relationships with the profitability ratio at 5%,5% and 10% levels of significance. This means that unit
increases in the variables shall bring about corresponding decreases in the profitability ratio of the industries
in Nigeria.
Guinness Table 4.4.18: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Guinness Nigeria Plc
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 6.652** (3.145)
2.320*** (26.798)
3.751 (1.773)
1.116** (3.185)
Accounts Receivable Ratio (AR)
0.657 (0.114)
-2.324** (-3.397)
1.714 (1.773)
0.045 (0.047)
Stock Turnover Ratio (STO)
-14.343 (-1.584)
5.335** (3.873)
-2.864* (-2.257)
-5.614 (-0.617)
Accounts Payable Ratio (AP)
-0.254 (-0.919)
-1.123*** (-7.990)
3.517* (2.246)
-0.045 (-0.990)
Cash Conversion Cycle Ratio (CCC)
-2.263 (-1.431)
0.0347 (0.0211)s
-0.202 (-0.424)
-0.411 (-1.571)
Liquidity Ratio (LQ) -3.030 (-1.988)
-0.255 (-0.174)
-11.721 (-2.060)
-0.524* (-2.076)
Debt Ratio (DT) (Control)
-235.70** (-2.995)
3.482*** (11.285)
0.611 1.779
-79.904*** (-6.128)
Sales Growth Rate (SL) (Control)
-0.016 (-1.080)
0.021 (0.763)
-1.206** (-2.990)
-0.003 (-1.227)
R2 0.929 0.991 0.710 0.989 Adjusted R2 0.843 0.981 0.202 0.967 F-Ratio 10.862*** 96.284*** 1.399 74.505
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Guinness
Nigeria PLC were summarized in Table 4.4.18 above. From the results presented above and out of the four
124
functional models of the multiple regression calculated, the Semi log Regression model was chosen because
it has the value with highest number of significant variables as well as a very significant F-ratio (96.284***)
indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2
value of 0.991 thus indicating that 99.1% variation in the profitability ratio (dependent variable) of Guinness
Nigeria PLC was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that STO had significant positive relationships with the industries’
profitability ratio at 1% level of significance. This implies that a unit increase in STO shall bring about
corresponding increase in the profitability ratio of Guinness Nigeria PLC. On the other hand, the industries’
AP and LQ had significant but negative relationships with the profitability ratio at 10% and 1% levels of
significance respectively. This means that unit increases in the variables shall bring about corresponding
decreases in the profitability ratio of the industries in Nigeria. CCC had positive but non significant
relationship with profitability ratio.
Table 4.4.19: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Nigerian Breweries PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.376 (2.034)
-2.822 (-1.515)
-2.995** (-4.134)
-0.445 (-1.358)
Accounts Receivable Ratio (AR)
-1.012 (-0.423)
-3.989* (-2.438)
-1.409 (-2.007)
-0.714 (-0.169)
Stock Turnover Ratio (STO)
0.112 (0.880)
-8.315** (-4.189)
0.147 (0.360)
0.028 (0.215)
Accounts Payable Ratio (AP)
-0.103 (-1.018)
5.440** (3.245)
-0.371 (-0.393)
-0.186 (-1.037)
Cash Conversion Cycle Ratio (CCC)
0.102 (0.121)
-1.461 (-0.900)
-0.148 (-0.348)
-0.010 (-0.060)
Liquidity Ratio (LQ) -0.037 (-0.184)
5.855*** (5.028)
0.765 (0.163)
-0.059 (-0.164)
Debt Ratio (DT) (Control)
168.586*** (5.898)
1.271 (1.634)
-0.208 -0.905
55.133 (1.089)
Sales Growth Rate (SL) (Control)
0.002 (0.716)
-0.931 (-2.035)
0.466 (1.222)
0.003 (0.556)
R2 0.999 0.935 0.710 0.948 Adjusted R2 0.996 0.820 0.202 0.857 F-Ratio 421.425*** 8.168** 1.399 10.403**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Nigerian
Breweries PLC were summarized in Table 4.4.19 above. From the results presented above and out of the
four functional models of the multiple regression calculated, the Linear regression model was chosen
125
because it has the highest number of significant variables as well as a very significant F-ratio (421.425***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.999 thus indicating that 99.9% variation in the profitability ratio (dependent
variable) of Nigerian Breweries PLC was accounted for by the explanatory (independent) variables
considered in the analysis. Specifically the results showed that AR, and AP had significant negative
relationships with the industries’ profitability ratio at 1% and 5% levels of significance respectively. This
implies that a unit increase in the variables shall bring about corresponding decrease in the profitability ratio
of Nigerian Breweries PLC. On the other hand, the company’s STO and CCC all had significant but positive
relationships with the profitability ratio at 5% levels of significance. This means that unit increases in the
variables shall bring about corresponding increases in the profitability ratio of the industries in Nigeria. LQ
had negative and non significant relationship with profitability ratio.
AVON Table 4.4.20: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Avon Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.344 (1.316)
0.105 (1.142)
-1.010 (-5.179)
-1.144** (-3.021)
Accounts Receivable Ratio Ratio(AR)
-1.293** (-4.108)
-0.876*** (-5.995)
1.431*** (4.623)
-3.283*** (-7.194)
Stock Turnover Ratio (STO)
-0.050 (-0.188)
0.019 (0.451)
-0.280** (-3.114)
0.182 (0.473)
Accounts Payable Ratio (AP)
0.652 (1.255)
0.493*** (4.692)
-1.065*** (-4.623)
0.2881** (3.823)
Cash Conversion Cycle Ratio (CCC)
0.390 (1.041)
0.037 (0.510)
0.204 (-1.340)
0.693 (1.276)
Liquidity Ratio (LQ) 0.007 (0.521)
0.062 (1.018)
-0.044 (0.344)
0.013 (0.623)
Debt Ratio (DT) (Control)
0.186 (0.913)
0.058 (2.033)
0.100 1.656
0.321 (1.088)
Sales Growth Rate (SL) (Control)
-1.943E-5 (-0.841)
0.007 (0.483)
-0.014 (-0.453)
-3.264E-5 (-0.974)
R2 0.825 0.962 0.975 0.947 Adjusted R2 0.520 0.896 0.932 0.853 F-Ratio 2.702 14.565** 22.851*** 10.122**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
126
The results of multiple regression analysis for the variable influencing the profitability ratio of Avon Nigeria
PLC were summarized in Table 4.4.20 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the Double log Regression model was chosen
because it has the highest number of significant variables as well as a very significant F-ratio (22.851***)
value which indicated that the choice model fitted the analysis. Furthermore, the results of the analysis
revealed an R2 value of 0.975 thus indicating that 97.5% variation in the profitability ratio (dependent
variable) of Avon Nigeria PLC was accounted for by the explanatory (independent) variables considered in
the analysis. Specifically the results showed that AR and CCC had significant positive relationships with the
industries’ profitability ratio at 1% level of significance. This implies that a unit increase in AR and CCC
shall bring about corresponding increase in the profitability ratio of Avon Nigeria PLC. On the other hand,
the industries’ STO,AP and LQ all had significant but negative relationships with the profitability ratio at
1% levels of significance. This means that unit increases in the variables shall bring about corresponding
decreases in the profitability ratio of the industries in Nigeria.
Beta Table 4.4.21: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Beta Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.035 (0.038)
0.048 (0.051)
-1.290 (-0.577)
1.972 (0.582)
Accounts Receivable Ratio Ratio(AR)
-0.397 (-0.144)
0.106 (0.150)
0.223 (0.133)
-1.230 (-0.123)
Stock Turnover Ratio(STO)
-0.941 (-0.480)
-0.667 (-0.625)
-1.589 (-0.628)
3.380 (0.474)
Accounts Payable Ratio (AP)
0.572 (0.349)
0.392 (0.597)
-0.193 (-0.124)
-5.088 (-0.854)
Cash Conversion Cycle Ratio (CCC)
0.494 (0.300)
-0.144 (-0.483)
-0.170 (-0.303)
-5.812 (-0.969)
Liquidity Ratio (LQ) 0.194 (0.359)
0.553 (0.491)
-2.449 (-0.917)
-1.904 (-0.971)
Debt Ratio (DT) (Control)
1.449 (0.249)
-0.011 (-0.160)
0.373 2.259
9.827 (0.464)
Sales Growth Rate (SL) (Control)
1.229E-7 (0.072)
-0.019 (-0.345)
-0.012 (-0.094)
8.502E-7 (0.137)
R2 0.296 0.240 0.700 0.347 Adjusted R2 -0.935 -1.090 0.174 -0.797 F-Ratio 0.241 0.180 1.331 0.303
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
127
The results of multiple regression analysis for the variable influencing the profitability ratio of Beta Nigeria
PLC were summarized in Table 4.4.21 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the Double log model was chosen because it has the
highest number of significant variables as well as a very significant F-ratio (1.331***) value which indicated
that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2 value of
0.700 thus indicating that 70.0% variation in the profitability ratio (dependent variable) of Beta Nigeria PLC
was accounted for by the explanatory (independent) variables considered in the analysis. Specifically the
results showed that AR, AP CCC and LQ had significant negative relationships with the industries’
profitability ratio at 1% level of significance. This implies that a unit decrease in the variables shall bring
about corresponding decrease in the profitability ratio of Beta Nigeria PLC. On the other hand, the
industries’ STO had significant and positive relationship with the profitability ratio at 1% levels of
significance. This means that unit increase in the variable shall bring about corresponding increase in the
profitability ratio of the industries in Nigeria.
INCAR Table 4.4.22: Multiple Regression Analysis showing the relationship between Profitability ratio and AR, STO, AP, CCC, LQ, DT and SL of Incar Nigeria PLC
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 0.287 (1.486)
-0.001 (-0.006)
0.497 (0.399)
-1.022 (-1.244)
Accounts Receivable Ratio (AR)
0.466 (1.022)
-0.070 (-0.271)
0.010 (0.025)
0.723 (0.373)
Stock Turnover Ratio (STO)
-0.289 (-0.496)
-0.707 (-1.369)
-0.628 (-0.261)
0.582 (0.235)
Accounts Payable Ratio (AP)
0.117 (0.212)
0.013 (0.022)
-1.061 (-0.393)
0.618 (0.262)
Cash Conversion Cycle Ratio (CCC)
-0.255 (-0.671)
0.356 (1.149)
0.423 (0.293)
-0.264 (-0.163)
Ratio (LQ) -0.080 (-1.187)
-0.245 (-1.172)
-0.395 (-0.405)
-0.140 (-0.486)
Debt (DT) (Control) -4.132 (-1.254)
0.290 (0.853)
1.631 1.029
-12.527 (-0.892)
Sales Growth Rate (SL) (Control)
0.000 (-0.777)
-0.060 (-0.445)
-0.127 (-0.201)
0.000 (-0.032)
R2 0.494 0.664 0.738 0.334 Adjusted R2 -0.116 0.075 -0.820 -0.832 F-Ratio 0.337 1.127 0.892 0.286
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui
128
2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables
The results of multiple regression analysis for the variable influencing the profitability ratio of Incar Nigeria
PLC were summarized in Table 4.4.22 above. From the results presented above and out of the four
functional models of the multiple regression calculated, the double log regression model was chosen because
it has the highest number of significant variables as well as a very significant F-ratio (0.892***) value which
indicated that the choice model fitted the analysis. Furthermore, the results of the analysis revealed an R2
value of 0.738 thus indicating that 73.8% variation in the profitability ratio (dependent variable) of Incar
Nigeria PLC was accounted for by the explanatory (independent) variables considered in the analysis.
Specifically the results showed that STO, AP and LQ had significant negative relationships with the
industries’ profitability ratio at 1% level of significance. This implies that a unit increase in the variables
shall bring about corresponding decrease in the profitability ratio of Incar Nigeria PLC. On the other hand,
the company’s CCC had significant but positive relationship with the profitability ratio at 1% levels of
significance. This means that unit increases in the variables shall bring about corresponding increase in the
profitability ratio of the company in Nigeria. However AR had positive but non significant relationship with
profitability ratio.
4.4 Test of Hypotheses
After obtaining the results of the four functional multiple regression models, decisions were therefore taken
on which among them should be chosen as the best fit model in the analysis. The choice models were then
used in the interpretation of the results. Decision and choice of the best fit model were fundamentally based
on the following: a) the one with highest number of significant variable b) significance of F-ratio which
measures the fitness of a model in using the independent variables to explain the dependent variable c) the
magnitude of the coefficient of multiple determinations (R2). Although decisions on the choice of models
were based mostly on ones with highest number of significant variables, result of the analysis must
necessarily show significant F-ratio. The coefficients of multiple determination (R2) were employed in the
study to quantify extent of variation in the dependent variable (profitability ratio) caused by the explanatory
(independent) variables considered in the study. Furthermore, the analysis were conducted at 1%, 5% and
10% levels of significance respectively denoted as ***, ** and *
The test of hypotheses were carried out as follows:-
Step 1 Re – statement of hypotheses in null and alternate
Step 2 Statement of Decision Criteria
Step 3 Presentation of pooled regression result
Step 4 Decision
129
Hypotheses 1
Ho: Accounts payable ratio has no significant and positive impact on corporate profitability
H1: Accounts payable ratio has significant and positive impact on corporate profitability
The decision criteria is to is to accept H0 if the sigh of the accounts payable coefficient is negative and the
value of t-statistics is significant otherwise reject H0 and accept Hi
4.5.1.8: ALL MANUFACTURING FIRMS IN NIGERIA Table 4.5.1.8: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of all Manufacturing firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 1.137 (3.399)
1.004 (0.214)
-0.539*** (-5.799)
-0.770*** (-12.957)
Accounts Receivable Ratio (AR)
0.000 (0.007)
1.480 (0.423)
0.843*** (0.622)
0.001 (1.601)
Stock Turnover Ratio (STO)
-1.561 (-0.806)
-7.493 (-1.427)
-0.730*** (-0.589)
-0.031 (-0.796)
Accounts Payable Ratio (AP)
1.081 (0.591)
2.993 (0.675)
-0.416*** (-2.457)
0.066* (1.800)
Cash Conversion Cycle Ratio (CCC)
-0.401 (-0.341)
1.939 (.554)
0.015*** (0.013)
-0.022 (-0.946)
Liquidity Ratio (LQ) -0.869 (-0.638)
-14.860** (-2.260)
-0.428*** (-3.281)
-0.020 (-0.732)
Debt Ratio (DT) (Control)
-1.694 (-0.215)
-4.655* (-1.838)
-.170*** (-3.404)
0.282* (1.800)
Sales Growth Rate (SL) (Control)
-2.040E-5 (-0.108)
-2.195 (-1.007)
0.036 (0.841)
-5.304E-7 (-0.142)
R2 0.005 0.055 0.123 0.058
Adjusted R2 -0.022 0.029 0.100 0.032
F-Ratio .198 2.117** 5.152*** 2.232**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables Source: Researchers Compilation See Appendix 26.
Decision: This hypothesis was used to test the payables of firms to their suppliers (creditor), Double log regression model result showed the coefficient of payable was negative but significant. Hence, we reject the alternate hypothesis and conclude that accounts payable ratio in Nigeria manufacturing firms is negative but significant. Hypothesis 2
Ho: Accounts receivable ratio has no significant and positive impact on corporate profitability
H1: Accounts receivable ratio has significant and positive impact on corporate profitability
130
4.5.1.8: ALL MANUFACTURING FIRMS IN NIGERIA Table 4.5.1.8: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of all Manufacturing firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 1.137 (3.399)
1.004 (0.214)
-0.539*** (-5.799)
-0.770*** (-12.957)
Accounts Receivable Ratio (AR)
0.000 (0.007)
1.480 (0.423)
0.843*** (0.622)
0.001 (1.601)
Stock Turnover Ratio (STO)
-1.561 (-0.806)
-7.493 (-1.427)
-0.730*** (-0.589)
-0.031 (-0.796)
Accounts Payable Ratio (AP)
1.081 (0.591)
2.993 (0.675)
-0.416*** (-2.457)
0.066* (1.800)
Cash Conversion Cycle Ratio (CCC)
-0.401 (-0.341)
1.939 (.554)
0.015*** (0.013)
-0.022 (-0.946)
Liquidity (LQ) -0.869 (-0.638)
-14.860** (-2.260)
-0.428*** (-3.281)
-0.020 (-0.732)
Debt Ratio (DT) (Control)
-1.694 (-0.215)
-4.655* (-1.838)
-.170*** (-3.404)
0.282* (1.800)
Sales Growth Rate (SL) (Control)
-2.040E-5 (-0.108)
-2.195 (-1.007)
0.036 (0.841)
-5.304E-7 (-0.142)
R2 0.005 0.055 0.123 0.058
Adjusted R2 -0.022 0.029 0.100 0.032
F-Ratio .198 2.117** 5.152*** 2.232**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables Source: Researchers Compilation See Appendix 26.
Decision: Accounts receivable refers to the receivables (debtors) that the firm will collect from its customers. Double
log regression model results show that the coefficient of the variable was positively and significantly related
to the profitability of manufacturing firms in Nigeria.
Hypothesis 3
Ho: There is no significant and positive impact of cash conversion cycle on profitability of the Nigeria
quoted manufacturing firms
H1: There is significant and positive impact of cash conversion cycle on profitability of the Nigeria quoted manufacturing firms
131
4.5.1.8: ALL MANUFACTURING FIRMS IN NIGERIA Table 4.5.1.8: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of all Manufacturing firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 1.137 (3.399)
1.004 (0.214)
-0.539*** (-5.799)
-0.770*** (-12.957)
Accounts Receivable Ratio (AR)
0.000 (0.007)
1.480 (0.423)
0.843*** (0.622)
0.001 (1.601)
Stock Turnover Ratio (STO)
-1.561 (-0.806)
-7.493 (-1.427)
-0.730*** (-0.589)
-0.031 (-0.796)
Accounts Payable Raio (AP)
1.081 (0.591)
2.993 (0.675)
-0.416*** (-2.457)
0.066* (1.800)
Cash Conversion Cycle Ratio (CCC)
-0.401 (-0.341)
1.939 (.554)
0.015*** (0.013)
-0.022 (-0.946)
Liquidity Ratio (LQ) -0.869 (-0.638)
-14.860** (-2.260)
-0.428*** (-3.281)
-0.020 (-0.732)
Debt Ratio (DT) (Control)
-1.694 (-0.215)
-4.655* (-1.838)
-.170*** (-3.404)
0.282* (1.800)
Sales Growth Rate (SL) (Control)
-2.040E-5 (-0.108)
-2.195 (-1.007)
0.036 (0.841)
-5.304E-7 (-0.142)
R2 0.005 0.055 0.123 0.058
Adjusted R2 -0.022 0.029 0.100 0.032
F-Ratio .198 2.117** 5.152*** 2.232**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables Source: Researchers Compilation See Appendix 26.
Decision:
This hypothesis was used to test the influence of cash conversion cycle on profitability of the
Nigeria quoted manufacturing firms. The coefficient was positive but not significantly related. The null
hypothesis was rejected.
Hypothesis 4
Ho: There is no relationship between stock turnover ratio and firms profitability.
H1: There is relationship between stock turnover ratio and firm profitability.
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4.5.1.8: ALL MANUFACTURING FIRMS IN NIGERIA Table 4.5.1.8: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of all Manufacturing firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 1.137 (3.399)
1.004 (0.214)
-0.539*** (-5.799)
-0.770*** (-12.957)
Accounts Receivable Ratio (AR)
0.000 (0.007)
1.480 (0.423)
0.843*** (0.622)
0.001 (1.601)
Stock Turnover Ratio (STO)
-1.561 (-0.806)
-7.493 (-1.427)
-0.730*** (-0.589)
-0.031 (-0.796)
Accounts Payable Ratio (AP)
1.081 (0.591)
2.993 (0.675)
-0.416*** (-2.457)
0.066* (1.800)
Cash Conversion Cycle Ratio (CCC)
-0.401 (-0.341)
1.939 (.554)
0.015*** (0.013)
-0.022 (-0.946)
Liquidity Ratio (LQ) -0.869 (-0.638)
-14.860** (-2.260)
-0.428*** (-3.281)
-0.020 (-0.732)
Debt Ratio (DT) (Control)
-1.694 (-0.215)
-4.655* (-1.838)
-.170*** (-3.404)
0.282* (1.800)
Sales Growth Rate (SL) (Control)
-2.040E-5 (-0.108)
-2.195 (-1.007)
0.036 (0.841)
-5.304E-7 (-0.142)
R2 0.005 0.055 0.123 0.058
Adjusted R2 -0.022 0.029 0.100 0.032
F-Ratio .198 2.117** 5.152*** 2.232**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables Source: Researchers Compilation See Appendix 26.
Decision:
This hypothesis was used to test the stock held by the firm in form of raw materials, work-in-progress or finished goods. The Double log regresssion showed that the coefficient of the variable was negative but significantly related with the industries’ profitability ratio. This means that alternate hypothesis is rejected. Hypothesis 5 Ho: There is no relationship between liquidity ratio and profitability of Nigeria quoted manufacturing
firms. H1: There is relationship between liquidity ratio and profitability of Nigeria quoted manufacturing firms.
133
4.5.1.8: ALL MANUFACTURING FIRMS IN NIGERIA Table 4.5.1.8: Multiple Regression Analysis showing the relationship between Profitability and AR, STO, AP, CCC, LQ, DT and SL of all Manufacturing firms in Nigeria
Variables
Linear Regression
Semi Log Regression
Double Log Regression
Exponential Regression
Constant 1.137 (3.399)
1.004 (0.214)
-0.539*** (-5.799)
-0.770*** (-12.957)
Accounts Receivable Ratio (AR)
0.000 (0.007)
1.480 (0.423)
0.843*** (0.622)
0.001 (1.601)
Stock Turnover Ratio Ratio (STO)
-1.561 (-0.806)
-7.493 (-1.427)
-0.730*** (-0.589)
-0.031 (-0.796)
Accounts Payable (AP)
1.081 (0.591)
2.993 (0.675)
-0.416*** (-2.457)
0.066* (1.800)
Cash Conversion Cycle Ratio (CCC)
-0.401 (-0.341)
1.939 (.554)
0.015*** (0.013)
-0.022 (-0.946)
Liquidity Ratio (LQ) -0.869 (-0.638)
-14.860** (-2.260)
-0.428*** (-3.281)
-0.020 (-0.732)
Debt Ratio (DT) (Control)
-1.694 (-0.215)
-4.655* (-1.838)
-.170*** (-3.404)
0.282* (1.800)
Sales Growth Rate (SL) (Control)
-2.040E-5 (-0.108)
-2.195 (-1.007)
0.036 (0.841)
-5.304E-7 (-0.142)
R2 0.005 0.055 0.123 0.058
Adjusted R2 -0.022 0.029 0.100 0.032
F-Ratio .198 2.117** 5.152*** 2.232**
NB: 1.Profitability=Bo + Bi (AR)ii + Bi(STO)ii+ Bi(AP)ii+ Bi(CCC)ii + Bi(LQ)ii + B2DT(control)2i + B2SL(control)2i + Ui 2. Also, 1%, 5%, 10% levels of significance are represented by ***; ** and * respectively 3. Values in brackets are coefficients while those outside brackets are t-values of the variables 4. DT and SL are not considered in the interpretation because they are controls variables Source: Researchers Compilation See Appendix 26.
Decision: Liquidity refers to cash which is collected from customers so that there would be no difficulty in paying short term debts of the firms. And it also refers to the ratio of firms’ liquidity. Liquidity hypothesis was set to investigate whether the liquidity of firms has impact on its profitability. The Double log regression results show that the coefficient of the variable was negatively but significantly related to profitability.
4.5.1 Robustness Test of the Aggregated Data for the 22 Firms.
It is observed that profitability (dependent variable) has a significant relationship with profitability of companies under study. This implies that when receivables rate is low that profitability rate is also low, and their external long term debt is also low. Therefore when they have little to receive it also makes their profit not to be enough. Other independent variables like stock turnover, accounts payable and liquidity are not significantly related to profitability; This is because even when liquidity increases; profitability does not
134
have a corresponding increase, rather it decreases. This is not in consistent with the multiple regression result which shows the liquidity has a significant relationship with the profitably of these firms under study. The result of this test also shows that accounts payable has significant relationship with firm profitability. This is not in consistent with the multiple regression result which indicates that accounts payable has a significant relationship with the profitability of these firms Sales growth one of the control variables is also significantly related to the profitability of these firms (see Appendix 25).
4.5.2 Discussion of Findings
A finding from test of hypothesis 1 implies that as the payable increases in these firms, the impact on the financial performance (profitability) decreases. This is also in consistent with the study of Ganesu (2007) whose accounts payable was negatively related although it was not significant in his study on working capital management efficiency of firms from telecommunication equipment industry. Again Mathuva (2009) found that accounts payable had highly significant negative relationship on (NSE) profitability of 30 firms listed on the Nairobi stock exchange. A finding from test of hypothesis 2 means that null hypothesis was rejected. An interpretation to this result is that, as accounts receivables (debtors) increase, the impact on profitability also increases. This disagrees with the study of Mathuva (2009) in his study on the influence of working capital management components on corporate profitability in Nairobi. He found out that there was a highly significant negative relationship between receivable and profitability. In the test of hypothesis 3, it implies that as the firms flow of cash from suppliers to inventory, accounts receivable and back to cash increases, then the profitability ratio also increases. The interpretation of the result of test of hypothesis 4 means that, as the stocks unsold increase, there is a corresponding decrease in the profitability ratio of all manufacturing firms in Nigeria. Finally, the test of hypothesis 5 means that the alternate hypothesis was rejected. An interpretation to this result is that, as the unit increases in value, this variable brings about a corresponding decrease in the profitability ratio of the quoted companies. Therefore for the fact that some of them have cash to settle their obligations does not mean that they would make enough profit.
135
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter provides a summary of key findings of the study. Further contributions of the study were
discussed, and finally suggested direction for further research, and recommendations were offered.
5.2 Summary of Research findings
The empirical examination of the hypothesis developed from the conceptual framework presented in this
study reveals a mixed set of results..
1. Accounts Payable Ratio had significant negative relationship with industries profitability ratio of
companies under study, showing that as the value of this variable increases, the financial performance
(profitability) of the firm has a corresponding decrease.
2. Accounts Receivable Ratio had significant positive relationship with the profitability ratio of
manufacturing firms in Nigeria. This indicates that as the value of receivable of these manufacturing
firms increase, the profitability has a corresponding increase. Therefore, it implies that the more they
receive, the more profit they have. Unfortunately both receivables and profitability rates of both
companies are low
3. Cash Conversion Cycle Ratio (CCC) had positive but non-significant relationship with their profitability.
This shows that as the value of CCC increases it has a corresponding increase on profitability, even
though it is not significant.
4. Stock Turnover Ratio had significant negative relationship with industries profitability of all
manufacturing firms in Nigeria
5. Furthermore, Liquidity Ratio also had significant negative relationship with industries profitability. This
also means that when the value of liquidity increases then profitability decreases. It also goes ahead to
mean that even when the companies liquidity value is high that it does not have same influence on
profitability.
5.2.1 Comparison of findings with objectives of the study
This section compares the results of this study with objectives of the study. There is strong evidence from
the result which shows the achievement of the key goals originally set out for this study. This is
demonstrated as follows.
136
Research objective one
To determine the impact of accounts payable ratio on corporate profitability
Outcome of research analysis
As can be observed from the correlation and regression results arising from the study, objective one is
judiciously met. The study showed that the relationship between accounts payable ratio and profitability is
statistically negative but significant. This result is in consistent with the study of shine and Soenen (1998)
which found out that there was a strong negative relationship between lengths of the firm’s net trading cycle
and its profitability. This shows that when the payables of these companies increase, their profitability ratio
do not increase even when the average rate of their payables goes up, still the profit they make does not have
influence or impact on firms profitability in Nigeria manufacturing firms. The results are also in agreement
with the studies of Ganesu 2007, Muchina 2011 and Raheman and Nasr (2007).
Research objective two
To examine the impact of accounts receivable ratio on corporate profitability.
Outcome of the Research analysis
The correlation and regression results arising from this study confirm that the objective has been met. The
result showed that accounts receivable ratio positively associated with firm profitability. This result is also in
agreement with the study of Basley and Brigham (2005), Samiloglu and Demirqunes (2008) and Sharma and
Kamar (2011). This means that as the average rate of receivables goes higher, that the profitability rate also
increases accordingly. The only problem here is that if the rate of receivables increases much without a
corresponding increase in the liquidity position of the companies, bankruptcy would be experienced thereby
temporary or permanent short down may occur. This is because bad debt would occur. This is not in
consistent with the study of Mathuva (2009) which indicates that, there exists highly significant negative
relationship between AR and profitability.
137
Research objective three
To examine the impact of cash conversion cycle ratio on profitability
Outcome of Research Analysis
On the impact of cash conversion cycle ratio (CCC) on profitability, the study revealed a positive but non –
significant relationship. The result addresses the rate by which cash flows from suppliers, to inventory, to
account receivables and back into cash. This shows that when CCC increases, profitability increases. This
also shows that as the rate by which they receive from their debtors increases that this would reduce bad
debt and enough cash would be available for settlement of firms obligation. This result is not in consistent
with the study of Vaidy et al (1990) which states that short conversion cycle indicates that the firm is
collecting the receivable as quickly as possible and delaying the payment to suppliers as slowly as possible.
This leads to high net present value of cash flow and high firm value. Lyrondi and Lazardis (2000) in his
study found out that there was a positive and significant relationship between CCC and profitability ratio
among others. This is in consistent with the result of this study.
Research objective four
To investigate the relationship between stock turnover ratio and firm profitability.
Outcome of Research Analysis.
As can be observed from the correlation and regression results arising from this study, objective four is
judiciously met. The study showed that the relationship between stock turnover ratio and profitability was
statistically negative but significant. This result supports the study of Padachi (2006) he found out that high
investment in inventories and renewable is associated with lower profitability. This study also is not in
consistent with the study of Falope and Ajilore (2009) which concludes that there is a significant negative
relationship between net operating profitability and an average collection period inventory turnover in days.
The results of our descriptive statistic and inference drawn from our dataset document evidence that stocks
have highest average rate of 91% and lowest of 25% which shows that they do not have too much stock
except in Brewery which has average rate of 91%, Breweries Sub-Sector should watch it, so that they would
make more profit and also be more liquid to settle their obligations in future years
Research Objective five
To determine the impact of liquidity ratio on the profitability of Nigeria quoted manufacturing firm
Outcome of Research Analysis
On the impact of liquidity ratio on the profitability of Nigeria Manufacturing firms, the study revealed a
negative but significant relationship. This study is in consistent with the study of Josse et al (1996) and
Eljelly, (2004). They examined the relationship between profitability and liquidity, and found out that there
138
exists a significant and negative relationship between profitability and liquidity. Van Home and Wachowicz
(2004) pointed out that excessive level of liquidity may have a negative effect on a firm’s profitability,
whereas a low level of liquidity may lead to stock – outs resulting in difficulties in manufacturing smooth
operations. Singh (2004) also states that the liquidity position of any firm mainly depends upon accounts
receivable and payable policy as well as inventories. In a more current explanation of the operating cycle
theory, Model, the result addresses the function of any trading unit which is to procure materials, process
the same, sell the finished goods and realize money, and utilize the money so received, to procure materials
again and to continue the cycle all over again. If enough cash is not realized settlement of firm’s obligations
may be difficult. This study is not in consistent with the study of Arvit Mallik, Debashish and Debelas
(2005). They studied on the impact of working capital management policies on corporate performance of
Indian consumers. They found out that no established relationship exist between liquidity and profitability
for the industries as a whole, although majority of the companies revealed positive association between
liquidity and profitability. Generally, the study reveals that majority of the manufacturing firms in Nigeria
are not liquid enough to meet up with their short – term obligations.
139
5.3 Conclusion
The importance of efficient working capital management (WCM) is indisputable. Moreover, adequate WCM
is essential as it has a direct impact on the profitability of firms. An attempt has been made in the present
study to examine the relationship between working capital management and corporate profitability of
Nigerian manufacturing companies, for the period 2000 to 2011. Some promising investments with high rate
of returns had turned out to be failures and were frustrated out of business. Many companies had been either
temporarily or completely shot down, while many Nigerian workers had been forcefully thrown into
unemployment market, even when the companies are listed in the Nigerian stock exchange. Despite the fact
that working capital management are presumed to be vital for company’s survival there is still relatively
little known about the way companies actually manage their working capital for negative or positive
influence on their profitability. This Thesis aims at understanding the working capital management of
Nigerian firms, how the variables interrelate, and the extent to which these identified variables impact on
firm profitability. Our studies focus on working capital of quoted manufacturing companies in Nigeria using
carefully chosen qualitative research methodology. Empirical data were gathered through the annual reports
and statement of accounts of the selected companies and the Nigerian stock Exchange fact-book 2000-2011.
The results indicate that accounts payable ratio has negative and significant relationship with the industries
profitability. On the other hand accounts receivable ratio had positive and significant relationship, while
CCC ratio had positive but non-significant relationship with firm’s profitability. Stock turnover ratio and
liquidity ratio had negative and significant relationship with the companies’ profitability. Generally, working
capital management has negative significant relationship on firms’ profitability in Nigerian manufacturing
firms.
5.4. Recommendations
Based on the findings of this study, we made the following recommendations:
1. There should be a balance between liquidity and profitability. This is because the rate of liquidity in
almost all the companies under study was low. The managers of these companies should make sure
that they attach more importance to cash, so that at every point in time, they can have enough cash to
settle their financial obligations.
2. There should be more carefulness in handling of stocks/inventories. The companies should make
every effort to have enough stocks so that they would not experience stock-outs
3. In as much as it is good to make sales, it is also not encouraging to sell everything to avoid stock-
outs which may creep in, if there is regular huge sales as can be seen in all the companies under
study.
140
4. The companies should have a closer watch on their payables. There should also be increase of their
credit sales so that they don’t go bankruptcy after paying their creditors.
5. These companies under study should adjust their cost of sales so that they can be able to make
enough profit since no company can exist without making profit which is the aim of every business
organization be it private or public.
6. Specialized persons in the field of finance should be hired by these companies for expert advice on
the working capital management in the Nigerian manufacturing companies.
5.5 Contribution to knowledge
Some contributions emerged from this research.
1. First, the study contributed to the understanding of working capital management by examining the
variables such as accounts payable, account receivable, cash conversion cycle, stock turnover and
liquidity. This approach offers more light into working capital management variables and firm
profitability especially in decision making,
2. Secondly to the best of the researcher’s knowledge this is the first study to empirically examine the
impact of working capital management and corporate profitability of Nigerian manufacturing firms
following a different perspective in the measurement of the variables and how they influence
profitability of the firms under study. Previous researchers were looking at the number of days it could
take companies to receive from debtors, pay to creditors, convert their stocks into. Cash and keep their
stocks, but this study looks at the rate of payables, receivables cash conversion cycle, stock/inventory
turnover and liquidity, of these firms and how they influence the profitability of these companies
3. Thirdly, after looking at the findings of this research, managers of the companies under study would
have better insights on how to maximize their firms value by maintaining optimal level of these variables
under study in the future years, and by managing their accounts payable, accounts receivable, stocks ,ccc
and liquidity.
4. Finally, no previous study used the four functional models of multiples regression. This study used four
of them and in each sub-sector/company; decisions and choice of the best-fitted-model were
fundamentally based on the highest number of significant variable.
5.6 Recommended Areas for Further Study
Future research should address the limitations of this study. Several extensions of this study are possible.
First, some manufacturing firms were excluded because of non-availability of data. Therefore they should
come in, in the next research. The number of years was twelve. There should be an extension of the years to
twenty or more for better result.
141
1. The dimension other researchers on this topic followed i.e. the numbers of years accounts payable,
receivable, CCC, inventory turnover among others should be done for comparison using the same four
multiple regressions in the analysis of the descriptive statistics of variables.
2. The study did not include size as a control variable; therefore it should be included in future research,
that is differentiating between large, medium or small companies.
3. The study also did not touch other sectors of the economy other than only manufacturing. Other sectors
for instance Petroleum sector, banking sector among others should be studied in the future.
4. Further Research should also attempt to investigate the Non-listed manufacturing firms in Nigerian stock
Exchange
142
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Appendix 1
All Nigerian manufacturing firms quoted in Nigeria stock exchange.
Dn tyre and rubber plc
Incar Nigeria plc
R.T Briscoe Nigeria plc
Champion breweries plc
Golden guinea breweries plc
Guinness Nigeria plc
International breweries plc
Jos international breweries plc
Nigerian breweries plc
Premier breweries plc
Ashaka cement plc
Dangote cement plc
Cement company of northern (Nigeria) plc
Lafarge cement Mapco Nigeria plc
Nigeria cement company plc
Nigeria ropes plc
Nigeria wire industries plc
African paints (Nigeria) plc
Berger paints plc
Chemical and allied products plc
DN Meyer plc
Ipwa plc
Nigeria- German chemicals plc
Paints and coatings manufacturers Nigeria plc
Portland paints and products Nigeria plc
Premier paints plc
Beverage (West Africa)
Cadbury Nigeria Plc
Flour mill Nigeria Plc
Foremost Dairies Plc
Honey Well Flour Mills Plc
150
Multi -Trex Integrated Food Plc
National Salt Company (Nigeria) Plc
Nestle food Nigeria Plc
Nigeria Bottling Company
P.S Mandrides and company
Tantalizers Plc
UTC Nigeria Plc
Union Dicon Salt Plc
Abplast Product Plc
Avon Crowncaps and Containers Plc
Beta Glass company Plc
Greif Nigeria Plc
Nampak Nigeria Plc
Nigeria Bag manufacturing company Plc
Poly Products (Nigeria) Plc
W.A Glass Industries Plc
Evans Medical Nigeria
Fidson Healthcare Plc
Glaxo Smithkling Consumer Plc
May and Beker Nigeria
Neimeth International Pharma Plc
Pharma-Deko Nigeria Plc
151
Appendix 2
The selected manufacturing firms in Nigeria
Seven up Nigerian PLC.
Cadbury Nigeria PLC.
Flourmills Nigeria PLC.
Nestle Nigeria PLC.
Nigerian bottling Company
Aluminum Ertusion industries PLC.
B.O.C case PLC.
Nigeria Enamelware PLC.
First Aluminum Nigeria PLC.
Vita Foam Nigeria PLC.
Vono products PLC.
Evans medical PLC
May and Baker PLC.
Pharma-Deko PLC.
Benue Cement company PLC.
Berger paints Nigeria PLC.
Premier paints PLC.
Guinness Nigeria PLC.
Nigeria Breweries PLC
Avon PLC
Beta Nigerian Plc
Incar Nigeria Plc
152
Appendix 25
Test of robustness of the aggregated data for the 22 firms
Source: version 17.1 Analytical Software computations
VARIABLES coefficient Standard
error
T Value Significance
CONSTANT -1.983 .347 -5.719 .000
PROFITABILITY .504 .235 2.149 .037
ACCOUNT
RECEIVABLE -.941 .303 -3.101 .003
STOCK TURN OVER .696 .401 1.737 .089
ACCOUNT PAYABLE .011 .008 1.395 .170
LIQUIDITY RATIO .457 .620 .737 .465
DEBTS -1.527 .420 -3.632 .001
SALES GROWTH RATE .002 .013 .190 .850
R SQUARE .988
ADJUSTED R SQUARE .987 .
F stat 547.337 000a
153
Appendix 26 Pooled Data for the Twenty (22) Firms.
YEARS Profitability AR STO AP CCC LIQ DT SL
Seven Up 2000 0.113106 0.104605 0.355011 0.539652 -0.79006 1.03707 0.022355 0
2001 0.145218 0.099151 0.345423 0.475622 -0.72189 1.140194 0 19.69397
2002 0.271914 0.104627 0.293899 0.046028 -0.2353 1.21306 0.056991 46.30892
2003 0.218926 0.085761 0.311998 0.572622 -0.79886 1.073494 0.053993 20.13747
2004 0.160043 0.11591 0.309017 0.490233 -0.68334 1.178816 0.020987 5.029189
2005 0.108646 0.092035 0.305505 0.63048 -0.84395 0.986985 0.049627 16.12928
2006 0.099763 0.089625 0.277554 0.576789 -0.76472 1.122865 0.055393 27.23907
2007 0.090575 0.10751 0.258471 0.444438 -0.5954 1.330172 0.207219 23.72896
2008 0.103443 0.104442 0.226578 0.072584 -0.19472 1.442658 0.244824 11.94874
2009 0.069744 0.117126 0.24219 0.078429 -0.20349 1.143692 0.230766 14.03912
2010 0.078634 0.102726 0.298402 0.122955 -0.31863 0.992808 0.178027 17.79708
2011 0.062763 0.082543 0.254755 0.096812 -0.26902 1.057806 0.189346 24.4201
CardBury 2000 0.200304 1.699728 0.569838 0.930822 0.199068 1.165491 0.001468 -80.1463
2001 0.225111 0.105478 0.314446 0.134515 -0.34348 1.856179 0.233933 30.41862
2002 0.257688 0.238158 0.280602 0.195981 -0.23842 1.763929 0 21.04079
2003 0.078091 0.250475 0.254396 0.129152 -0.13307 1.861905 0 28.48299
2004 0.184423 0.17217 0.408579 0.203577 -0.43999 1.417961 0 7.661647
2005 0.120165 0.306413 0.294346 0.205678 -0.19361 1.689492 0 32.96009
2006 -0.19427 2.496323 0.565473 0.566632 1.364218 6.98E-07 0 -93.4763
2007 -0.16201 0.12335 0.203367 0.368705 -0.44872 0.339087 0 937.5683
2008 -0.11914 0.015954 0.208917 0.318636 -0.5116 0.401772 0 1118.764
2009 -0.09425 0.110462 0.179581 0.320557 -0.38968 1.213793 0 -89.4703
2010 0.006856 0.140663 0.171979 0.411355 -0.44267 1.170943 0 14.01166
2011 0.15077 0.147314 0.116713 0.387519 -0.35692 1.455637 0 16.93494
Flour Mills 2000 0.062955 0.084558 0.2701 0.383491 -0.56903 1.023931 0 -30.4156
2001 0.045528 0.105999 0.191431 0.354695 -0.44013 0.940391 0 30.2805
2002 0.121243 0.106399 0.171953 0.315678 -0.38123 0.986094 0.014902 40.04672
2003 0.079136 0.112289 0.22543 0.457299 -0.57044 0.667954 0.061983 -2.43955
2004 0.064044 0.116677 0.184553 0.410195 -0.47807 8.23164 0.077586 26.77674
2005 0.050743 0.09088 0.198615 0.373551 -0.48129 0.880119 0.091148 24.72301
2006 0.123591 0.040732 0.166926 0.332991 -0.45919 0.975365 0.094368 29.58734
2007 0.128599 0.042334 0.210144 0.101588 -0.2694 1.192708 0.043324 22.05919
2008 0.090501 0.042113 0.190231 0.077658 -0.22578 1.109464 0.130942 20.8133
2009 0.086655 0.029698 0.195372 0.063838 -0.22951 1 0.198082 41.05095
2010 0.170286 0.03076 0.195032 0.053804 -0.21808 1.00689 0.196633 14.73876
2011 0.10073 0.036113 0.234802 0.038454 -0.23714 1.327283 0.051569 15.57971
154
Nestle 2000 0.481512 0.036229 0.297164 0.041147 -0.30208 1.347511 0 -95.8007
2001 0.542715 0.038762 0.270756 0.061754 -0.29375 1.243079 0 41.07834
2002 0.538042 0.054848 0.243174 0.044004 -0.23233 1.32647 0 38.39675
2003 0.490925 0.02872 0.294709 0.121868 -0.38786 1.17994 0 25.80868
2004 0.455249 0.040198 0.211009 0.086666 -0.25748 1.072648 0 15.54538
2005 0.468611 0.033442 0.202017 0.065146 -0.23372 1.431038 0 20.64157
2006 0.433563 0.039647 0.240533 0.068358 -0.26924 1.57978 0 11.90268
2007 0.398252 0.052219 0.187945 0.086122 -0.22185 1.313177 0 14.58703
2008 0.406804 0.083199 0.204953 0.095891 -0.21764 1.382976 0.205094 17.52262
2009 0.311483 0.049805 0.267728 0.078163 -0.29609 0.99131 0.026949 32.03375
2010 0.302325 0.104984 0.193584 0.093108 -0.18171 1.026608 0.130988 17.25981
2011 0.240945 0.087637 0.173211 0.131958 -0.21753 0.937433 0.108809 22.28536
Nigerian Bottling Coy
2000 0.393536 0.813535 0.437983 0.263167 0.112385 1.120172 0.16795 -99.3784
2001 0.206563 0.005077 0.24116 0.262537 -0.49862 1.220904 0.016966 15400.98
2002 0.189522 0.068714 0.243749 0.340301 -0.51534 1.221606 0.00665 -82.223
2003 0.179394 0.000294 0.273788 0.289718 -0.56321 1.076829 0.001568 26064.26
2004 0.10425 0.021482 0.281922 0.262171 -0.52261 0.891911 0.0072 -98.9168
2005 0.081297 0.025977 0.284813 0.21873 -0.47757 0.740357 0.00155 16.59303
2006 0.041572 0.257943 0.24182 0.308382 -0.29226 0.668745 0.145827 -89.2371
2007 0.090393 0.022628 0.232747 0.261337 -0.47146 0.826391 0.17138 1048.382
2008 0.046941 0.033735 0.158216 0.024452 -0.14893 0.597816 0.127306 16.85642
2009 0.065202 0.044436 0.211645 0.274417 -0.44162 0.706016 0.140371 12.63138
2010 0.066978 0.045666 0.204737 0.267598 -0.42667 0.696161 0.925761 2.420694
2011 0.069759 0.050567 0.203583 0.25823 -0.41125 0.743172 1.345363 2.927982
Aluminium & Extrusion
2000 -0.17457 0.029729 0.470203 0.920084 -1.36056 0.644753 0.132355 149.3861
2001 0.027075 0.012892 0.375608 0.590613 -0.95333 0.726668 0.129362 107.6118
2002 #DIV/0! 0.030048 0.428695 0.72257 -1.12122 0.699932 0 -9.3444
2003 -0.10002 4.18E-08 0.39171 0.897196 -1.28891 0.54013 0.158807 0.055333
2004 -0.00359 0.020822 0.02797 0.294801 -0.30195 1.079555 0.574492 46.18196
2005 0.025307 0.017938 1.244713 1.63882 -2.86559 0.844166 0.489302 19.34508
2006 0.069129 5.012375 0.084038 0.309787 4.61855 0.330755 0 -99.8891
2007 0.123379 2.240985 0.112309 0.232824 1.895852 0.433575 0.089315 20.60725
2008 0.132689 0.003673 0.093191 0.374516 -0.46403 0.274149 0.066074 25.85389
2009 0.183129 0.00763 0.092482 0.298306 -0.38316 0.399638 0.022846 15.72687
2010 0.107863 0.00465 0.115071 0.03435 -0.14477 0.534355 0.030635 6.168729
2011 0.08267 0.002038 0.178404 0.042439 -0.2188 0.58354 0.043248 7.461922
155
BOC Gases 2000 0.189275 0.129458 1.109496 0.985073 -1.96511 1.288734 0 -65.9259
2001 0.168305 0.113898 0.534775 0.576817 -0.99769 1.317834 0 23.40414
2002 0.22658 0.138085 0.546158 0.641237 -1.04931 1.326488 0 16.84696
2003 0.204921 0.172972 0.53871 0.65538 -1.02112 1.27238 0 5.95848
2004 0.105303 0.132226 0.47935 1.304705 -1.65183 0.645921 0.026881 11.35017
2005 0.070344 0.164616 0.344353 1.158746 -1.33848 0.699019 0.021972 12.39635
2006 0.114355 0.191811 0.314032 0.102248 -0.22447 2.160556 0 16.88493
2007 0.148103 0.181155 0.222247 0.081418 -0.12251 2.276143 0.021285 41.95916
2008 1.608196 0.180411 0.216783 0.509058 -0.54543 2.340526 0 7.075497
2009 2.137864 1.921697 0.204774 4.716995 -3.00007 2.314747 0 -88.9498
2010 0.244451 0.154894 0.298365 0.070862 -0.21433 1.453875 0 944.6215
2011 0.230765 0.227649 0.26413 0.080281 -0.11676 1.723196 0 2.030441
First Aluminium 2000 0.034205 0.132706 0.458108 0.554159 -0.87956 1.138943 0.017556 37.45874
2001 -0.05427 0.166345 0.302798 0.574776 -0.71123 0.857254 0.097599 21.64458
2002 -0.07504 0.219035 0.322809 0.781648 -0.88542 0.743331 0.037068 3.929083
2003 0.060228 0.224367 0.31564 0.595556 -0.68683 0.986241 0 17.73386
2004 0.029809 0.14952 0.252935 0.459725 -0.56314 0.943693 0.067587 32.34261
2005 0.039676 0.141898 0.243504 0.427539 -0.52915 0.968488 0.038525 26.70396
2006 0.00423 1.475335 0.4414 0.66276 0.371175 0.931358 0.035696 -89.3193
2007 0.013302 0.152696 0.449109 0.76377 -1.06018 1.144051 0.032279 906.843
2008 0.054515 0.110184 0.47579 0.804198 -1.1698 0.995711 0.023784 -7.2634
2009 0.005564 0.110547 0.412092 0.507059 -0.8086 1.052736 0.02233 2.598037
2010 -0.02837 0.068609 0.372038 0.141473 -0.4449 1.025672 0 5.675414
2011 -0.02823 0.049816 0.340095 0.122823 -0.4131 1.023692 0 0.33763
Nigeria EnamelWare 2000 0.055609 0.01741 0.20302 0.228862 -0.41447 1.067457 0 -85.3937
2001 0.046812 0.019345 0.256754 0.277148 -0.51456 1.07913 0 29.50315
2002 0.044657 0.06316 0.238739 0.295182 -0.47076 1.106424 0 0.647805
2003 0.035341 0.11977 0.231017 0.490015 -0.60126 0.875633 0 6.281947
2004 0.027997 2.796533 0.275529 0.450197 2.070807 1.455332 0 -90.7981
2005 0.040731 0.202927 0.2981 0.446521 -0.54169 1.196748 0 985.5856
2006 0.037447 0.09133 0.257648 0.4743 -0.64062 12.3837 0 -11.4427
2007 0.031938 0.055467 0.253461 0.686061 -0.88405 1.222207 0 -0.28251
2008 0.032037 0.016147 0.231522 0.759021 -0.9744 1.223348 0 -3.75639
2009 0.091262 0.114505 0.132662 0.395962 -0.41412 1.164838 0 59.79402
2010 0.087434 0.013886 0.167634 0.01001 -0.16376 1.205792 0 -2.3203
2011 0.121361 0.021459 0.264007 0.027001 -0.26955 1.309605 0 0.345576
156
VITAFOAM 2000 4.91608 0.019497 0.238571 0.412514 -0.63159 1.259264 3.169866 -2.55417
2001 0.790495 0.029334 0.169614 0.325117 -0.4654 1.272154 0.428224 45.97292
2002 0.705918 0.701703 1.672614 3.511045 -4.48196 1.267943 0.493476 -88.9543
2003 0.696921 0.069525 0.196427 0.55307 -0.67997 1.301187 0.499186 946.024
2004 0.267607 0.070609 0.229983 0.358249 -0.51762 1.588526 0.2291 -6.07238
2005 0.089483 0.072786 0.332352 0.307538 -0.5671 1.628719 0.170737 -3.4377
2006 0.125305 0.071339 0.302631 0.392664 -0.62396 1.486494 0.095042 15.18871
2007 0.172302 0.044743 0.522885 0.567299 -1.04544 1.606318 0.088006 51.43039
2008 0.089192 0.045076 0.600511 0.604295 -1.15973 1.537045 0.136418 26.35814
2009 0.101853 0.048116 8.0196 6.538124 -14.5096 1.613712 0.070708 1.79639
2010 0.134751 0.071099 0.292533 0.10704 -0.32847 1.36147 0.003727 34.31674
2011 0.140849 0.063647 0.472434 0.487577 -0.89636 1.312799 0.006186 21.39204
Vono Products 2000 0.049152 0.320705 0.313411 0.68763 -0.68034 1.39601 0 -97.5338
2001 0.009833 0.364824 0.50508 0.914108 -1.05436 1.188563 0 -1.62607
2002 0.056677 0.185077 0.519558 0.708302 -1.04278 1.178557 0 21.44706
2003 0.06283 0.151616 0.588987 0.636499 -1.07387 1.296213 0 15.80251
2004 -0.80435 0.402993 0.284609 0.692139 -0.57375 0.55205 0 -35.5932
2005 -0.21107 0.079839 0.479424 1.074428 -1.47401 1.446484 0 -6.63423
2006 0.035496 0.092677 0.782238 3.427611 -4.11717 0.56206 0 14.03441
2007 -0.48964 0.108948 0.093453 0.554162 -0.53867 0.405234 0 365.3164
2008 -0.12629 0.097411 0.248673 1.116453 -1.26771 0.390741 0 -55.1426
2009 -0.12209 0.247299 0.167828 2.390098 -2.31063 0.273435 0 -28.894
2010 -0.18286 0.206023 0.215318 2.984274 -2.99357 0.390633 0.192664 -2.34065
2011 -0.13616 0.222578 2.289354 2.493004 -4.55978 0.370545 0 23.37397
EVANS MED 2000 0.034625 0.200615 0.500073 0.360476 -0.65993 0.650436 0 39.05726
2001 0.029339 0.121096 0.5048 0.303518 -0.68722 0.753833 0 22.50026
2002 0.065951 0.141989 0.578437 0.310066 -0.74651 0.965887 0 28.72962
2003 0.054482 0.225431 0.678359 0.318362 -0.77129 1.483619 0 29.93023
2004 -0.01729 0.194029 0.567678 0.177332 -0.55098 1.134561 0 54.04279
2005 0.028394 0.208988 0.806809 0.209004 -0.80682 1.237001 0 6.804
2006 0.04886 0.231519 0.832432 0.228932 -0.82985 1.193643 0 14.98197
2007 -0.08589 0.207648 1.041209 0.331742 -1.1653 0.973779 0 8.364676
2008 -0.08256 0.225626 0.724474 0.397054 -0.8959 0.842645 0 41.67471
2009 -0.24173 0.180554 0.665725 0.494238 -0.97941 0.609483 0 -21.0859
2010 0.031798 0.219795 0.537572 0.550305 -0.86808 0.99945 0 29.75194
157
2011 0.010873 0.348139 0.449646 0.534846 -0.63635 1.018916 0 -13.766
May & Baker 2000 0.066064 0.216198 0.563288 0.492636 -0.83973 2.015839 0 -76.212
2001 0.150865 0.30818 0.592333 0.709101 -0.99325 1.794236 0 12.51061
2002 0.070925 0.246188 0.493328 0.47496 -0.7221 2.073639 0 20.81539
2003 0.105454 0.191962 0.42294 0.452129 -0.68311 1.780608 0 39.65755
2004 0.094409 0.173053 0.429675 0.066085 -0.32271 2.217934 0.055875 6.763298
2005 0.07945 0.141987 0.427996 0.061357 -0.34737 2.33457 0.253096 5.056067
2006 0.067142 0.110614 0.538486 0.110545 -0.53842 2.330899 0.010669 12.84018
2007 0.103268 0.190242 0.324683 0.145158 -0.2796 1.691455 0 71.2864
2008 0.123612 0.102321 0.257904 0.105433 -0.26102 1.533621 0.06543 40.93948
2009 0.055926 0.114615 0.33705 0.089207 -0.31164 1.117355 0.069466 -15.3578
2010 0.045151 0.176017 0.454535 0.121577 -0.40009 0.987145 0.124106 0.754573
2011 0.048182 0.134381 0.314421 0.115176 -0.29522 0.713063 0.106008 4.275843
PharmaDeko 2000 -0.28955 0.354035 0.641718 2.368614 -2.6563 0.489257 0 -97.9008
2001 -0.0135 0.224999 0.439907 1.444802 -1.65971 0.568342 0 125.8291
2002 0.145708 0.23696 0.200375 0.962236 -0.92565 0.82401 0 78.30239
2003 0.12896 0.242828 0.18933 0.803619 -0.75012 1.222433 0 49.24213
2004 0.04528 0.355082 0.343871 1.147123 -1.13591 0.933075 0 16.74983
2005 0.096513 0.022456 1.719287 2.230389 -3.92722 1.015897 0 691.7326
2006 -0.24871 0.169461 0.165785 1.730685 -1.72701 0.366942 0 -88.4972
2007 -0.16012 0.226577 0.068243 2.452934 -2.2946 0.346626 0 21.81199
2008 -0.13097 27.87963 0.087971 1.865503 25.92615 0.347666 0 -85.3775
2009 0.199647 0.105479 3.121161 3.908496 -6.92418 0.204843 0 334.2857
2010 -0.2259 0.194207 0.408095 0.746414 -0.9603 0.509943 0.341589 -1.48885
2011 0.024817 0.042818 0.367094 0.453319 -0.7776 0.569618 0.361385 155.2044
Benue Cement 2000 -0.10239 0.257883 2.192216 3.416137 -5.35047 0.434935 0.049438 -37.2788
2001 -0.27937 0.309324 1.230452 2.4361 -3.35723 0.426871 0.324863 40.8999
2002 -0.48596 0.417114 1.730596 5.679208 -6.99269 0.200208 0.150877 -47.7203
2003 -0.47319 0.614822 0.158396 3.175943 -2.71952 0.065602 0.011947 -32.9348
2004 -0.12134 0 0 0 0 0 0 0
2005 -0.06948 0.243327 0.542425 2.003753 -2.30285 0.09676 0.039317 0
2006 0.061145 0.277999 0.120833 1.405418 -1.24825 0.26009 0.192292 50.53825
2007 0.050877 0.30078 0.14593 1.417717 -1.26287 0.124063 0.009276 -9.21796
2008 -0.49101 0.35029 0.122604 1.224063 -0.99638 0.198604 0.078497 -21.0928
158
2009 -0.09077 0.616123 2.725822 15.7122 -17.8219 0.147949 0.023004 46.62865
2010 -0.08465 0.618571 18.98004 12.11783 -30.4793 0.219682 0.015857 0.290235
2011 -0.1001 0.573751 17.44545 13.06192 -29.9336 0.183523 0.022139 5.599682
Berger Paints 2000 0.040842 0.225659 0.624668 0.711688 -1.1107 1.014841 0 -83.2194
2001 0.149082 0.015042 0.47907 0.520509 -0.98454 1.477945 0 1271.29
2002 0.104144 0.029615 0.623666 0.887832 -1.48188 3.70502 0 -5.96092
2003 0.027384 0.304388 0.344477 0.326061 -0.36615 2.768688 0 -89.8095
2004 0.101776 0.185786 0.374117 0.199954 -0.38828 1.070067 0 24.72222
2005 -0.03298 0.163377 0.345622 0.167599 -0.34984 0.780299 0 3.774959
2006 0.055236 0.127628 0.265374 0.145622 -0.28337 0.876194 0 20.1845
2007 0.105111 0.113769 0.316593 0.142209 -0.34503 1.074013 0 -1.09792
2008 0.119973 0.067981 0.214389 0.107955 -0.25436 1.495632 0 11.39888
2009 0.141529 0.086668 0.22027 0.117995 -0.2516 1.663335 0 -6.1101
2010 0.199542 0.075142 0.354545 0.098011 -0.37741 1.966739 0 15.83131
2011 0.09189 0.041688 0.35829 0.15541 -0.47201 2.004241 0 -6.61135
Premier Paints 2000 0.031775 0.220372 0.090847 0.235076 -0.10555 1.359711 0 4563.757
2001 -0.01297 0.188504 0.126416 0.244712 -0.18262 0.739277 0 23.95242
2002 -0.04412 0.269069 0.140902 0.347373 -0.21921 1.449073 0 14.48656
2003 -0.07276 0.260572 0.144533 0.371088 -0.25505 0.985215 0.019877 16.9352
2004 -0.03865 0.139605 0.094559 0.216466 -0.17142 0.772511 0 -6.88844
2005 0.033165 0.15418 0.134137 0.245011 -0.22497 1.056572 0.111846 1.911046
2006 0.059564 0.075673 0.215312 0.321044 -0.46068 0.828867 0 7.420559
2007 0.042811 0.119833 0.203366 0.043095 -0.12663 0.66117 0 -8.40279
2008 0.042385 0.114605 0.219709 0.36006 -0.46516 1.179122 0.187566 26.29176
2009 -0.07876 0.138092 0.205996 0.566807 -0.63471 2.726085 0 -99.9049
2010 -0.31878 0.089328 0.075798 0.798313 -0.78478 0.235933 0.104972 -25.703
2011 -0.33598 0.121703 0.128706 0.918849 -0.92585 0.321282 0.457924 10.04324
Guiness 2000 2.247028 0.063444 0.46237 0.819921 -1.21885 1.685756 0 8008.361
2001 2.373897 0.044891 0.423432 0.886579 -1.26512 1.635157 0 34.14634
2002 2.475667 0.068125 0.050217 0.809349 -0.79144 1.530557 0 48.61583
2003 2.782544 0.044512 0.402423 0.717165 -1.07508 1.275824 0 28.98812
159
2004 3.225994 0.074574 0.599913 0.863647 -1.38899 1.279394 0 24.68406
2005 2.295494 0.030967 0.575155 0.577808 -1.122 1.354934 0 -1.36635
2006 2.175907 0.060227 0.464473 0.649673 -1.05392 1.457979 0 14.49534
2007 2.26966 0.106997 3.725657 4.611446 -8.23011 1.558861 0 16.0547
2008 1.985516 0.120222 0.361333 0.481756 -0.72287 1.419631 0 11.09354
2009 1.874018 0.102132 0.362241 0.386106 -0.64622 1.293253 0 28.87745
2010 0.242115 0.121209 0.261913 0.383124 -0.52383 1.220605 0.01573 22.67995
2011 0.283829 0.14664 0.254067 0.383899 -0.49132 1.214107 0.014344 13.07172
Nigerian Brewries 2000 0.187997 0.062143 0.565708 1.330301 -1.83387 0.708631 0 -79.1743
2001 0.20132 0.083651 0.596933 1.47014 -1.98342 0.862055 0 49.25301
2002 0.157326 0.131633 7.780552 2.274686 -9.9236 0.729929 0.000232 11.49092
2003 0.12917 0.048905 0.053668 2.048696 -2.05346 0.676646 0 32.02814
2004 0.135789 0.038671 0.55443 1.606228 -2.12199 0.725712 0 34.72451
2005 0.178149 0.022242 0.030644 0.517608 -0.52601 0.71536 0 5.119847
2006 0.217247 0.055284 0.301781 0.389265 -0.63576 1.036894 0 7.726235
2007 0.307862 0.067882 0.307374 0.079327 -0.31882 1.621691 0 29.45506
2008 0.342777 0.037818 0.265875 0.270764 -0.49882 1.193719 0 17.61987
2009 0.386958 0.021859 0.253101 0.278626 -0.50987 0.889194 0 24.93084
2010 0.392346 0.034679 0.215119 0.25832 -0.43876 0.8976 0 13.18821
2011 5.305932 0.351184 0.237804 0.261137 -0.14776 0.927166 0.033012 -89.4971
AVON PLC 2000 0.054965 0.555617 0.011554 0.508627 0.035435 1.31452 0.055777 -88.4762
2001 0.032087 0.611437 0.013375 0.407161 0.1909 1.784638 0.008413 5.584376
2000 0.054965 0.555617 0.011554 0.508627 0.035435 1.31452 0.055777 -5.28902
2001 0.032087 0.611437 0.013375 0.407161 0.1909 1.784638 0.008413 5.584376
2002 0.034374 0.659469 0.013494 0.563668 0.082306 1.400889 0.462862 27.76296
2003 0.090574 0.70063 0.012133 0.70371 -0.01521 1.160882 1.085291 17.55113
2004 0.223693 0.299225 0.258162 0.520169 -0.47911 0.617504 0.037123 -95.1786
2005 0.572117 1.137001 0.298611 0.360075 0.478315 3.365619 0.034906 -32.7802
2006 0.057896 0.435837 0.247423 0.445365 -0.25695 1.147865 0.001356 5224.027
2007 0.066702 0.443952 0.291999 0.441639 -0.28969 1.212119 0.006041 0.373878
2008 0.059838 0.71869 0.454237 0.572514 -0.30806 11.33401 0.043699 -5.78091
2009 0.054331 0.753927 0.445897 0.611554 -0.30352 1.211586 0.009772 34.5
2010 0.019567 0.138631 0.565879 0.029333 -0.45658 1.193709 0.033012 42.184
2011 0.024522 0.142663 0.386691 0.033419 -0.27745 1.264542 0.014386 5.670915
160
BETA GLASS 2000 0.34548 0.083305 0.56962 1.227466 -1.71378 0.802416 0 -88.9941
2001 0.078735 0.068712 0.604436 1.467064 -2.00279 0.734279 0 37.17858
2000 0.34548 0.083305 0.56962 1.227466 -1.71378 0.802416 0 -27.1023
2001 0.078735 0.068712 0.604436 1.467064 -2.00279 0.734279 0 37.17858
2002 -0.20646 0.068338 0.682502 1.413858 -2.02802 0.78736 0 7.764795
2003 -0.02561 0.096698 0.542303 0.947552 -1.39316 0.59046 0 72.72593
2004 0.157362 0.170062 0.400486 0.848305 -1.07873 0.838901 0.024207 167196.2
2005 0.060098 0.166906 0.511382 0.927035 -1.27151 0.833077 0.011074 6.973513
2006 -0.00129 0.11657 0.533668 1.128768 -1.54586 0.63737 0 7.744265
2007 0.018956 0.096404 0.523652 0.865036 -1.29228 0.836468 0.001097 26.1771
2008 0.191068 0.060386 0.424581 0.437903 -0.8021 1.13941 0.072007 47.56765
2009 0.236358 0.084422 0.374341 0.514075 -0.80399 1.106158 0.051679 0
2010 0.113309 0.180154 0.286072 0.079622 -0.18554 2.168452 0 -99.9059
2011 0.114813 0.134937 0.248194 0.118584 -0.23184 2.399884 0 13.95163
INCAR 2000 -0.02023 0.211613 0.316651 0.26557 -0.37061 2.2511 0 -99.2106
2001 0.020077 0.166968 0.390773 0.255363 -0.47917 2.497668 0 -3.05902
2002 -0.17036 0.245182 1.273556 0.787858 -1.81623 1.285351 0 -51.1953
2003 -0.1598 0.107984 0.271873 0.318117 -0.48201 1.453845 0 251.1909
2004 0.573668 0.438517 0.300339 0.296444 -0.15827 0.579928 0 3.041686
2005 0.075197 0.717616 0.298611 1.706349 -1.28734 0.710216 0.167333 -32.7802
2006 0.02504 3.710533 0.45748 0.410707 2.842346 4.71814 0.155922 -5.70097
2007 -0.01791 5.182049 0.499541 0.603812 4.078696 16.20123 0 17.00406
2008 0.012452 0.639761 0.233821 0.427836 -0.0219 2.854802 0 133.2685
2009 0.140629 0.15798 0.355916 0.93647 -1.13441 4.322751 0 290.9986
2010 0.303165 0.146475 0.359142 0.857778 -1.07045 4.828046 0 10.71456
2011 0.346697 0.129538 0.215988 0.5631 -0.64955 0.523721 0 16.27126
