THE EFFECTS OF WORKING CAPITAL MANAGEMENT ON THE PROFITABILITY OF LISTED NIGERIAN CONGLOMERATE COMPANIES
SUNUSI GARBA (SPS/11/MAC/00001)
BEING A DISSERTATION SUBMITTED TO THE SCHOOL OF POST GRADUATE STUDIES, BAYERO UNIVERSITY, KANO, IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE (M.SC) DEGREE IN ACCOUNTING
DECEMBER, 2014
DECLARATION I hereby declare that, this work is the product of my own research efforts, undertaken under the supervision of Dr. Junaidu Muhammad Kurawa and has not been presented anywhere for the award of a degree or a certificate, except in partial fulfilment of the requirements for the award of Master of Science degree in Accounting, Bayero University Kano. All sources and materials used have been duly acknowledged in the references, and any act of commission or omission is not with intent and is highly regretted.
_____________________ Sunusi Garba SPS/11/MAC/00001
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APPROVAL/CERTIFICATION This thesis entitled "The Effects of Working Capital Management on the Profitability of Listed Nigerian Conglomerate Companies" by Sunusi Garba has been read and approved as meeting the requirements for the award of Master of Science Degree in Accounting, Bayero University, Kano and is approved for its literary presentation and contribution to knowledge.
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Dr. Junaidu Muhammad Kurawa
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Supervisor
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Prof. Bashir Tijjani
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Internal Examiner
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Dr. Junaidu Muhammad Kurawa
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Head of Department
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Dean, School of Postgraduate Studies
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External Examiner
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ACKNOWLEDGEMENTS Praised be to Allah the Almighty, the Master of the universe, He who with His Blessing and Majesty good things is accomplished. I am very grateful to Him for sparing my life and blessing me with good health and the wisdom to complete this dissertation. I owe all my efforts and success unto Him. And to our Master Prophet Muhammad (PBUH) the seal of prophets who brought the light to the entire world. I am happy to take this opportunity to express my gratitude to those who have been helpful to me in completing this project. My deepest appreciation goes to the Almighty God and then to my supervisor, Dr. Junaidu Muhammad Kurawa, for his priceless guidance, tolerance and comprehensive supervision and assistance, throughout the period of this research study. And to my Internal Examiner, Professor Bashir Tijjani, may Allah reward them abundantly. My special thanks also go to Professor Kabiru Isa Dandago, Professor Ali Suleiman Kantudu, and Professor Muhammad Liman Muhammad, and to all other staff of the Department of Accounting, Bayero University, Kano, and especially Dr. Ishaq Alhaji Isma'il, Dr. Hannatu Sabo, Dr. Ibrahim Magaji Barde, Dr. Kabir Tahir Hamid, Dr. Sadiq Rabiu Abdullahi, Mal. Garko, and Mal. Mukthar Bako, for their support and priceless contributions towards the successful completion of this work. I also appreciate the support and immense contributions of my course mates to the successful completion of this work, especially Muhammad Adamu Chamo, Zaharaddeen Abdullahi, Ayagi Sunusi Ridwan, Abdulrahman Saheed, Bashiru Mahmud, Ibrahim Aliyu Gololo, Ibrahim Hamidu, Nuhu Otaru Isah, Yusuf Abubakar, Nasiru Muhammed, Ismail Lukman Adesina, Usman Alih, Mesbah Naser Ashana, Bashir Mahmud, Jamilu Umar Lawan, Sani Saidu, Umar Salisu, Zaharaddeen Salisu Maigoshi, Kabir Ibrahim, and Muhammad Salisu just to mention but a few. I would also like to express my sincere appreciation to my elder Brothers, Barrister Tijjani Garba Ringim and Engr. Abdulhadi Garba Ringim for the concern, care and fatherly advice given to me. Their words and contributions really count a lot towards the accomplishment of this work. I would also like to express my appreciation to my friends and colleagues too numerous to be mentioned, who have significantly contributed in making this research a success. Few among
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them include Shamsudeen Almustapha, Jibril El-yakub, Aliyu Shehu Kiru, Ismail Abdulmumin, Mukthtar Sabo Kima, I thank them all for their advice and well wishes. Finally, a very special gratitude goes to my parents, Mallam Garba Dandibi Maizare and Malama Suwaiba Garba for their love, care and support. May the Almighty Allah forgive all their short comings and grant them Al-jannatul Firdaus (Amin). Also my special thanks go to my cousins Barrister Ibrahim Abubakar Mansur, Bello Abubakar Mansur, Bashir Habib, Nasir Habib, Basiru Inuwa, and to all my sisters for their encouragement and prayers during the entire programme, in general, and the writing of this dissertation, in particular. Lastly, exceptional gratitude goes to my beloved wife Hassana and my beloved daughter Maryam. I thank them all for the patience, care and prayers throughout the period of the study and beyond.
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DEDICATION In the memory of my late Father (Mallam Garba Dandibi Maizare), my late Grandmother (Hajiya Gwoggo) and my late cousin (Amina Garba (Inna), May the Almighty Allah forgive them and grant them Aljannatul Firdaus, Amin.
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TABLE OF CONTENTS Contents DECLARATION .......................................................................................................................ii APPROVAL/CERTIFICATION ............................................................................................. iii ACKNOWLEDGEMENTS ...................................................................................................... iv DEDICATION .......................................................................................................................... vi TABLE OF CONTENTS .........................................................................................................vii LIST OF TABLES ..................................................................................................................... x ABSTRACT.............................................................................................................................. xi CHAPTER ONE ........................................................................................................................ 1 INTRODUCTION ..................................................................................................................... 1 1.1
Background to the Study ............................................................................................. 1
1.2
Statement of the Research Problem ............................................................................ 2
1.3
Objectives of the Study ............................................................................................... 5
1.4
Research Hypothesis ................................................................................................... 6
1.5
Significance of the Study ............................................................................................ 6
1.6
Scope of the Study....................................................................................................... 8
CHAPTER TWO ....................................................................................................................... 9 LITERATURE REVIEW .......................................................................................................... 9 2.1
Introduction ................................................................................................................. 9
2.2
The Concept of Working Capital Management .......................................................... 9
2.3.1
Working Capital Management Components ......................................................... 10
2.3.2
Inventory Management .......................................................................................... 10
2.3.5
Cash Management ................................................................................................. 14
2.4 2.5.1
The Operating Cycle Concept ................................................................................... 18 The Concept of Corporate Profitability ................................................................. 19 vii
2.5.2
Measures of Corporate Profitability ...................................................................... 20
2.6
Conglomerate Business ............................................................................................. 22
2.7
Empirical Studies on Working Capital Management and Profitability..................... 23
2.7.1
Nigerian Empirical Studies on Working Capital Management and Profitability .. 23
2.7.2
Other Countries Studies on Working Capital Management and Profitability ....... 27
2.8
Theoretical Framework on Working Capital Management....................................... 42
2.8.1
Monetary Theory ................................................................................................... 42
2.8.2
Financial Theory .................................................................................................... 44
2.8.3
Cash Cycle Theory ................................................................................................ 45
CHAPTER THREE ................................................................................................................. 48 RESEARCH METHODOLOGY............................................................................................. 48 3.1
Introduction ............................................................................................................... 48
3.2
Research Design ........................................................................................................ 48
3.3
Population of the Study ............................................................................................. 49
3.4
Sample Size ............................................................................................................... 50
3.5
Sources and Methods of Data Collection .................................................................. 50
3.6
Variables of the Study ............................................................................................... 50
3.6.1
Dependent Variables.............................................................................................. 51
3.6.2
Explanatory Variables ........................................................................................... 51
3.7
Method of Data Analysis........................................................................................... 53
3.7.1
Descriptive Statistics ............................................................................................. 53
3.7.2
Pearson Correlation ............................................................................................... 53
3.7.3
Multiple Regression Techniques ........................................................................... 54
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3.8
Model of the Study .................................................................................................... 57
CHAPTER FOUR.................................................................................................................... 59 RESULTS AND DISCUSSION .............................................................................................. 59 4.1
Introduction ............................................................................................................... 59
4.2
Descriptive Statistics of the Variables of the Study .................................................. 59
4.3
Correlation between the Variables of the Study........................................................ 60
4.4
Impact of Inventory Turnover Period on Profitability .............................................. 62
4.5
Impact of Average Collection Period on Profitability .............................................. 66
4.6
Impact of Average Payment Period on Profitability ................................................. 70
4.7
Impact of Cash-to-Cash Cycle on Profitability ......................................................... 74
CHAPTER FIVE ..................................................................................................................... 78 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS............................................ 78 5.1
Summary ................................................................................................................... 78
5.2
Conclusions ............................................................................................................... 82
5.3
Recommendations ..................................................................................................... 83
REFERENCES ........................................................................................................................ 85 APPENDIX 1 ........................................................................................................................... 92 LISTED NIGERIAN CONGLOMERATE COMPANIES DATA SHEET ............................ 92 APPENDIX 2 STATA VERSION 12.0 GENERATED RESULTS ................................. 102
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LIST OF TABLES Table 3.1: Population of the Study Table 3.2: Working Population of the Study Table 4.1: Descriptive Statistics of the Variables Table 4.2: Correlation Coefficients of the Variables Table 4.3: Regression Results of the Impact of Inventory Turnover Period on Profitability Table 4.4: Regression Results of the Impact of Account Receivable Period on Profitability Table 4.5: Regression Results of the Impact of Account Payable Period on Profitability Table 4.6: Regression Results of the Impact of the C2C Cycle on Profitability
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54 58 64 66 68 71 74 77
ABSTRACT The management of Working Capital is one of the most important financial decisions of a firm. Efficient level of working capital should be present for smooth running of business regardless of the nature of business. Working Capital Management is important part in firm financial management decision. The ability of the firm to continuously operate in longer period is depends on how they deal with investment in working capital management. The optimal of Working Capital Management could be achieve by firm that manage the trade off between Profitability and liquidity. This study examines the relationship between Working Capital Management and firm Profitability for the Nigerian conglomerate sector. The study is carried out based on the historical panel data analysis. To achieve this objective; an ex-post factor research design was employed. Data were generated from secondary sources, specifically, the annual reports and accounts of quoted firms from 2003 to 2012. The population of the study consists of six Conglomerate companies listed on the Nigerian Stock Exchange. Descriptive statistics, Pearson correlation, as well as fixed-effect and randomeffect generalized least square (GLS) regression techniques alongside with Hausman Specification Test as the decision rules were utilized as tools of analysis in the study. The findings establish that Working Capital Management affects Nigerian conglomerate companies‟ Profitability however, insignificantly. The study recommends that companies should sufficiently plan and control their operations, amend the shortfalls as noted, consider the principles of finance in their decision making, utilize the services of professionals in complex business areas, and perform periodic stock taking.
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CHAPTER ONE INTRODUCTION 1.1
Background to the Study
Finance is regarded as the lifeblood of any business. Effectual financial management is fundamental for the business endurance and expansion. Most of the decisions on the part of the finance managers concerning this important resource have a bearing on the performance, risk as well as the market value of the firms. The financial management decisions of companies are mainly concerned with three (3) key areas: capital budgeting, capital structure, and Working Capital Management (WCM). Of these key areas, the WCM is an area of enormous importance for each company as it almost influences its overall Profitability and liquidity (Appuhami, 2008). A firm's performance largely depends on the manner its working capital as been handled (Karadagli, 2012). A firm should efficiently and effectively handle its working capital. If it is incapable to handle efficiently and effectively of its working capital then this may possibly result in not only decrease in Profitability but may perhaps have great consequence like financial crisis for a firm. It is an issue of greater concern and value how can firms handle their working capital in a manner that will produce ultimate success of a firm. Working Capital Management is fundamentally regarding how much working capital the company ought maintain should they go for zero risk management, or can they make an effort a bit of daredevilry in their working capital management. Working Capital Management entails decisions about company‟s assets and liabilities- what they comprise, how they are utilized, and their mixture have an effect on the risk against return features of the company (Attari & Raza, 2012). The management of working capital is one of the most vital financial decisions of a company. Efficient level of working capital have to be there for smooth management of business in spite of nature of the business. In order to handle working capital 1
efficiently, companies have to be conscious of how long it take them, to convert their goods and services into cash on average. This time-span of time is normally known as the Cash-toCash Cycle. High costs of production as a result of poor infrastructure; the dearth of infrastructure has been one of the major threats to the profit maximization of many Conglomerate. Power and logistics costs continue to constitute a rising portion of operational and administrative costs (Ademola, 2011). Due to the identified challenges, performance of conglomerate companies trails that of single product focused companies in Nigeria; available financial information for selected conglomerate and single product focused companies in Nigeria suggests that conglomerate companies have operated less efficiently than single product focused companies have over the last five years (Ademola, 2011). Hence, this study attempts to uncover the effects of Working Capital Management on Profitability of the listed Conglomerate Firms in Nigeria. 1.2
Statement of the Research Problem
Profit maximization is the ultimate objective of every firm and preserving liquidity is an important objective. The complexity of Working Capital Management is to accomplish the dual objectives optimally within an operating period if profit rises at the cost of liquidity and this may possibly lead to severe trouble to firms. Thus, to resolve such dilemma, there should be concession between the two goals of companies. Single goal will never be achieved at the expense of other as both goals have their individual significance to companies. If companies are not concerned regarding Profitability, they could not last for a longer time. On the other hand, if companies do not worry about liquidity, they might face problem of bankruptcy or insolvency.
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Poor Management of the working capital will end in cash flow problems painted by an company beyond its established overdraft boundary, failing to pay creditors on time, and being incapable to claim discounts for timely payment. In the long run, a company with inadequate working capital will never be capable to meet up its current responsibilities and will be forced to close down business even if it remains profitable on paper. However, discovering optimal levels of inventory, receivables, as well as payables where total investment and opportunities cost are reduce and recalculating the C2C cycle according to these best possible points offer more absolute and precise insights into the effectiveness of working capital management. The problem therefore is to determine how the efficiency of working capital management, relates to the firm's Profitability. A group of scholars such as Deloof (2003); Lazaridis & Tryfonidis (2006); Lee, Song, & Lee (2009); Nobanee & AlHajjar (2009); and Raheman & Nasr (2007) are of the view that accounts payable days is inversely related with firm Profitability. That is as the account payable of the firm increase its Profitability will decrease. While, another group such as Alipour (2011); Gill, Biger & Mathur (2010); Mathuva (2009); and Napompech (2012) are of the view that accounts payable day is positively with firm Profitability. That is an increase of account payable days of the firm will increase its Profitability. With regard to relationship between inventory conversion and firm Profitability scholars such as Ali (2011); Gill, Biger & Mathur (2010); Padachi (2006); Rimo & Panbunyuen (2010); Soekhoe (2012); and Warnes (2013) attest a positive relationship. That is increase in inventory days will lead to increase in profits of a firm. Despite the fact that scholars like Alipour (2011); Deloof (2003); Lee, Song, & Lee (2009); Panigrahi (2013); and Usama (2012) are postulating that there is a reverse relationship between the inventory turnover and Profitability.
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So also the relationship between account receivable conversion and firm Profitability researchers such as Lee, Song, & Lee (2009); Ogundipe, Idowu, & Ogundipe, (2012); Ramachandran & Janakiraman (2009); and Sharma & Kumar (2011) claim a positive relation between account receivables and firm Profitability. However, Ahmadi, Arasi & Garajafary (2012); Raheman & Nasr (2007); Raheman, Afza, Qayyum & Bodla (2010); Ray (2012); Soekhoe (2012); Suhail & Lahcen (2011); and Usama (2012); argue a negative relationship between Profitability, and the Number of days Account receivables. Likewise, with regard to the relation between C2C cycle and firms Profitability a number of studies have provided empirical evidence that managers can improve firms‟ Profitability through efficient C2C cycle. There is however lack of harmony among researchers regarding how each variable of Working Capital Management affects corporate Profitability, for Example, Lyroudi & Lazaridis (2000); Onwumere, Ibe & Ugbam (2012); Soekhoe (2012); and Leeper & Chambers (2013) claim that an increase in C2C cycle improves company performance in terms Profitability. Conversely, Deloof (2003); Lazaridis & Tryfonidis (2006); Garcia-Teruel & Martinez-Solano (2007); and Warnes (2013) showed that increasing C2C cycle leads to decreasing Profitability in the companies. Furthermore to the best of the researcher‟s knowledge there are few or possibly absent of studies, on sectoral basis, that investigated the effect of Working Capital Management on firm Profitability in Nigerian Conglomerate Sector. These twin concerns are the motivating factors for this study. Lack of or perhaps missing of empirical evidence on the effect of Working Capital Management on firm Profitability in case of the Nigerian Conglomerate sector (to the best of the researcher‟s knowledge), as well as lack of general agreement regarding the influence that Working Capital Management variables have on corporate Profitability provided the reason for this study. The study therefore, is an attempt to fill this
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gap and will examine the relationship between Working Capital Management and firm Profitability for the Nigerian Conglomerate sector. In the light of the foregoing the current study aims to address the followings questions: a)
How does firms‟ day of inventory affects the Profitability of the listed Conglomerate Firms in Nigeria?
b)
How does firms‟ days receivable outstanding affects the Profitability of the listed Conglomerate Firms in Nigeria?
c)
How does firms‟ days payable outstanding affects the Profitability of the listed Conglomerate Firms in Nigeria?
d)
How does firms‟ overall C2C cycle affects the Profitability of the listed Conglomerate Firms in Nigeria?
1.3
Objectives of the Study
The primary aim of this research work will be evaluating the effect of Working Capital Management on corporate Profitability from Conglomerate Firms in Nigeria which are listed on the Nigerian Stock Exchange. In order to achieve the above mentioned objective which is the main objective, the specific objectives developed as guides to the study are: i.
to examine the effects of Days of Inventory on the Profitability of the listed Conglomerate Firms in Nigeria
ii.
to examine the effects of Days Receivable Outstanding on the Profitability of the listed Conglomerate Firms in Nigeria.
iii.
to determine the effects of Days Payable Outstanding on the Profitability of the listed Conglomerate Firms in Nigeria. 5
iv.
to verify the effects of Overall C2C cycle on the Profitability of the listed Conglomerate Firms in Nigeria.
1.4
Research Hypothesis
The researcher based on the objectives set above, developed the following null hypothesis Hypothesis 1: HO1: Inventory conversion period does not have significant effect on the Profitability of the listed Conglomerate Firms in Nigeria. Hypothesis 2: HO2: Receivable conversion period does not have significant effect on the Profitability of the listed Conglomerate Firms in Nigeria. Hypothesis 3: HO3: Payables deferral period does not have significant effect on the Profitability of the listed Conglomerate Firms in Nigeria. Hypothesis 4: HO4: C2C cycle does not have significant effect on the Profitability of the listed Conglomerate Firms in Nigeria. 1.5
Significance of the Study
The significance of this study can be viewed from different angles. Firstly, this study will be of great relevance empirically as the researcher is not aware of any work that examined the effects of Working Capital Management on the Profitability of Nigerian Conglomerate sector. The study will no doubt, be of immense interest and benefit not only to the companies in Nigerian Conglomerate sector, but also to the Nigerian economy as a whole. To the Nigerian 6
Conglomerate companies, the study will lead to improvement in their Working Capital Management positions, thereby contributing to enhance their value. Furthermore, the last decade was characterised by an economic recession that affected the entire world. This economic downturn stifled effective demand which posed great challenges to the survival of most companies. Financial managers will benefit from this study as they play very important role towards the management of working capital with a view to driving performance for the survival of their companies. Shareholders, investors and analysts will also find this study useful in evaluating their investment options. The findings of this study will hopefully assist financial managers in planning and designing policy relating to credit purchase and credit sales on the basis of working capital management. Lastly, the findings of this research may also be of use to the prospective researchers in their academic and nonacademic endeavors. By attempting to address an important limitation of the above researches, this study aims to provide additional insights into the effects of Working Capital Management on the Profitability of Nigerian Conglomerate sector. It is hoped that the evidence would serve as vital quantitative information into the cauldron of policy as well as an addition to the existing body of empirical literature from a developing stock exchange such as that of Nigeria. This work contributes to existing literature in the sense that, from available literature, to the best of the researcher's knowledge, there is no previous study that addresses the effect of Working Capital Management on the Profitability of Nigerian Conglomerate. The study will also contribute to the existing literature and serve as a reference material in the field of corporate finance, especially with regards to the role of sound Working Capital Management policies in boosting corporate Profitability.
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1.6
Scope of the Study
The study focuses on the relationship between Working Capital Management and the Profitability of Nigerian Conglomerate companies. The study specifically consider the relationship between Inventories, Receivables, Payables conversion period as well as C2C cycle as Working Capital Management components and the Return on Asset (ROA) as a Profitability Proxy. The study covers ten year period 2003 to 2012. The period is considered adequate in making a justifiable conclusion. This is in consistent with Charitou, Elfani, & Lois (2012), Karadagli (2012), Takon (2013), Shah & Chaudhry (2013), and Panigrahi (2013).
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CHAPTER TWO LITERATURE REVIEW 2.1
Introduction
This section is set out to review the write ups of other scholars on the topic that is being studied. The section will examined most existing literatures on Working Capital Management of Conglomerate Firms in Nigeria industry. In order to make the reader be better equipped so as to apprehend and fully appreciate the effect of Working Capital Management on corporate Profitability from Conglomerate Firms in Nigeria; emphasis is placed on some unique components of Working Capital Management in financial statement. 2.2
The Concept of Working Capital Management
Diverse definitions have been attempted by different scholar with regard to Working Capital Management, for instance, Akinsulire (2011), defined working capital as those items that are essential for the day-to-day production of goods to be sold by a company. Likewise, Duman & Sawathanum (2009) refer to working capital as the amount the firm‟s current assets exceed its current liabilities. They further posited that current assets includes cash, account receivables, inventory, market securities and prepaid expenses; whereas current liabilities comprises short-term debt, account payable, accrued liabilities and other debts. As believed by Gardner (2004) working capital is that which establishes company‟s capability to meet up its immediate commitments by means of current assets as opposite to on loan funds. Thus, working capital funds the cash to cash cycle of a business. For the time being, cash to cash cycle can be thought of as the time during which returns generated by operations is yet to be received in cash. Clearly, insufficiency in working capital is not advantageous as it implies that the company will have alternative to external funds. And in excess of it is not good; either extreme value
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tied up in current assets could mean that the company is creating sub-optimal use of its capital, or a working capital of nothing appears to optimal, however it is the revise of its composition along with its quantity that will disclose whether working capital is at an optimal stage or not, this is where liquidity appears (Gardner, 2004). Scholars have identified a mixture of Working Capital Management performance metrics. Four of the most regularly employed ones are the following; inventory turnover, account payable turnover, accounts receivable turnover and C2C metric. Each of these metrics discloses a diverse aspect about working capital position. Concisely, the study tries to show that working capital is the backbone of liquidity management. Hence, the health of an organization is determined by its proper working capital management.
2.3.1 Working Capital Management Components
Inventory, accounts receivables, and accounts payables together make up the well-known cash conversion cycle (CCC) which symbolize the quantity of time that each net input Naira is tied up in the production and sales route prior to being transformed into cash. Efficient management of working capital needs effective administration of all three areas of working capital; Revenue Management (Account Receivable), Expenditure management (Account Payable) and Supply Chain Management (Inventory Management). 2.3.2 Inventory Management
Inventory or stocks are a crucial make-up of current assets. Manufacturing firms usually contain in their inventory: raw materials, works in progress or finished goods. In most cases, it is a balancing to keep inventory for sales and having less inventory to improve working capital. When there are less stock when a customer‟s demand has to be met immediately, the company will lose out on revenue if the customer‟s demand is not met. On the other side of 10
things, holding too much inventory will have an opportunity cost and may give rise to obsolescence. The management of inventory is one of the most difficult tasks for working capital managers who, if they may well decide, would like to reduce the inventory to the extent that will possibly shorten the C2C cycle and decrease costs. The danger of reducing an inventory downwards to a stage close to zero is that it increases the chance of running out of resources required in the production or running short of finished goods at some point in a high demand. Such condition would be expensive for every company because of the revenues they would lose (Maness & Zietlow, 2005). The average number of day‟s inventories stand for the time that goods are held by the firms before they are sold. So as to assist cut down the C2C cycle, a lower number of days are better. The average quantity of inventory is received by taking the sum of the opening and closing balance of inventory for a year, and divide by two, to obtain the average. The average amount of inventory is then divided by the cost of goods sold to observe how much part of cost goods sold that comes from the inventory. In order to get the outcome of the C2C cycle in days the amount given is multiplied by the average amount of days a year, 365 (Rimo & Panbunyuen, 2010). The most important recognition factor of the Working Capital Management is inventory turnover (especially in manufacturing companies). Research conducted by Padachi (2006) shows that about 67% of the time between cash outflow from purchase of goods to cash inflow from the sale of goods depends on the inventory turnover. We can say that manoeuvre power of companies in accounts receivable period and accounts payable period is much lower than inventory turnover. Because firms can easily reduce this period using advanced
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technologies and modernization and have tried to lower the C2C cycle which it will lead to increase return on assets (Padachi 2006). 2.3.3 Accounts Receivable Management When a company sells goods or services on credit, it records this as a receivable in the ledgers and the balance sheet. Company gets it cash within a given period that it gives the customer, this is called the credit period. Company manage the receivables by intimating the credit period to the buyer so that the buyer will know when to pay. Accounts receivable management, which is as well famous as debtor management, is a firm providing their customers a particular credit period to pay for commodities. These credit terms, which are called trade credit, can help relieve customer‟s financial frictions. Customers who buy commodities on trade credit are called sundry debtors for the company. Account receivable is a key element in working capital management. Derived from the exchange of credit sale, controlling and managing account receivables happen to be extremely significant. Kumar (2010), describes the meaning of debtor management as a method of making decision which transmits to the venture in the business debtors. And the objective of debtor management is to encourage the sales and in the meantime reduce the risk of not receiving money from the customers. But where debtor management is in a poor circumstance, working capital ratio could be traumatic which originates the requirements for additional capital input or increased debt. In order to accomplish successful account receivables management, focus have to be on two factors. On the one hand, firm wishes to distinguish which credit strategy will go well with their business. Credit strategy gives firms a directive about how to handle the customer and how much credit they have to relax to their customers. With a liberated credit strategy, the
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sale and Profitability of a firm may enhance largely, but the danger of bad debts or interest foregone may also rise. With a harsh credit strategy, the security and liquidity of a firm may increase, but Profitability of the firm may possibly go down. Achieving the most favourable level of security and Profitability is the single duty of financial manager. On the other hand, company should know their customers well, that is what is called credit analysis. 2.3.4 Accounts Payable Management Account payable is the liability that comes from credit purchases and is posted as a sum payable by the buyer and account receivable to the seller. Most companies, specially retail and manufacturing, buy goods on credit and record it as a liability that has to be paid. A company can extend its credit policy based on the relationship between the suppliers. However it should be noted that it is a form of short term debt. Effective management of trade credit is important and company should make sure suppliers are receiving the payment on time to make them satisfied. The common procedure for optimizing the control of accounts payable entail the timing of payments. Firms have to try to extend the period of payment as long as possible as they can use the benefit of their creditors funding their business until payment has been completed. A different argument for extending the period for payment is that the producing firms must require some period to convert their acquired raw material into commodities they can get traded and obtain money in return (Maness & Zietlow, 2005). A number of creditors offer their clients price cut as an effort to get them to reimburse their receivables earlier than maturity time which might sound attractive but this is not all the time the most beneficial alternative. To evade being deceive by theses discount offers, firms must vigilantly consider each discount offer they get to observe that it is favourable in terms of their circumstances. For a discount to be favourable to the consumer the discount rate have to be higher than the interest rate the firms would need to compensate for a loan over the equivalent time as the discount time (Maness & Zietlow, 2005). If there is no discount offer agreed, firms must use the entire credit time and pay their outstanding on due date. Paying 13
behind due date must all the time be keep away from except if the firm has crashed in economic problems and there is no other alternative (Dolfe & Koritz, 2000). The grounds for this is that deferred payments can end in avoidable costs as overdue fees. Suppliers tendering credit transaction will produce account receivable, while, clients accepting the credit transaction will create accounts payable. Account payable, which arises when company acquires commodity on loan, is the payment for sellers for commodity. One value of receiving credit transaction from creditors is that firm can decrease a number of investments in WCM and accumulate a few funds. Exploiting the account payable and elongating the payment term may perhaps be an economical benefit for companies. Accounts payable or trade creditors as sometimes called form a major part of the outside debt of small businesses which have less access to long-term funds. According to Bates, Kahle, & Stulz (2009), trade creditors form a heterogeneous group, and sums other than pure trade credit and debt are often included. As an example, hire purchase due, sums due for wages, rents, sums due for purchase tax, and income tax are all included, and loans to associated concerns, if of a fairly short-term nature, are frequently included in debtors. Because longterm credit is frequently unavailable to the small business, short-term credit is especially important. 2.3.5 Cash Management Cash in the current assets section can have multiple uses. It can be used to buy stock, pay salaries and purchase fixed assets etc. It is safe for organizations to hold big amounts of cash for companies cash needs as they do not have to raise an overdraft, call on shareholders to put in additional capital or raise debt. Large amounts cash which is not used for buying stocks, to fund the expansion of business or to pay dividends gives the company a lost opportunity to earn a return. This cash can be invested in a savings account, fixed deposit or government bonds for example, to earn an interest.
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Teigen (2001) describe cash management as a branch of treasury management, which is defined as a part of the key tasks of the essential finance management team. The specific tasks of a typical treasury function are Cash Management, Risk Management, Hedging and Insurance Management, Accounts Receivable Management, Accounts Payable Management as well as Bank Relations. This definition is consistent with the (Owolabi & Alu, 2012) classification of cash management areas (but risk management is not included). According to them the responsibilities of cash management can be divided into Cash balance management, Cash gathering, Cash mobilization and concentration, Cash disbursement and Banking system design decision processes. In their specification of the notion “cash balance management” includes Management of cash position, Short-term borrowing, Short-term investing and Cash forecasting. In this context the management of the firm‟s cash position could include managing accounts receivable, improving cash flow, transferring funds, and controlling cash disbursements. The concept of cash-to-cash is a basic financial concept. Numerous definitions of C2C cycle (which is synonymous with CCC) have been in earlier studies. Moss & Stine (1993) as citied in (Attari & Raza 2012) define C2C cycle as “days between account payable and accounts receivable”. Gallinger (1997) as citied in (Churchill & Mullin, 2001) put it somewhat differently; “the cash conversion system measures the number of days the firm‟s operating cycle requires costly financing to support it”. Operating cycle can be thought of as the numbers of days of sales are invested in inventories and receivables. Churchill & Mullin (2001) put at another way as; “the length of time, company cash is tied up in working capital before the money is finally returned when customers pay for the products sold or services rendered”.
15
The cash-to-cash, by reflecting the net time interval between actual cash expenditures on a firm‟s purchase and the ultimate recovery of cash receipts firm product sales, establishes the period of time required to convert a Naira of cash disbursements back into a Naira of cash inflow from a firm‟s regular course operations. Evaluating the interrelated cash inflowoutflow pattern underlying a more complete approach to liquidity analysis requires an additional a more complete approach to liquidity analysis requires an additional flow indicator of current liabilities, which is account payable created by short term deferral of these operating expenditures. C2C cycle is a unique financial performance metric that indicates how firm is managing their capital across the supply chain. Admittedly, these definitions ignore depreciation and places income taxes within operating expenses. Hence, the components of C2C cycle are inventory turnover days plus accounts receivable days minus accounts payable days (Gardner, 2004). The C2C metric is important from both accounting and supply chain management perspectives. It can be used for accounting purposes in the determination of firm liquidity and organizational valuation. A short C2C cycle, implying that fewer days, cash are tied up in working capital and not offset by “free” financing in the form of deferred payments, result in more liquidity for the firm (Deloof, 2003). Also, a shorter conversion cycle results in a higher present value of net cash flows generated by the assets and therefore, a higher firm value, for supply chain management activities, this metric involve three links in the supply chain. It bridges across in bound material or finished goods activities with suppliers through manufacturing, wholesalers and distribution operations and continues through the out bound sales activities with customers bridges material activities through suppliers, production operations, distribution functions and outbound activities, it becomes one of the first multiadic metric that may be used to further supply chain management (Farris, Staberhofer & Losbicher, 2010). 16
The C2C metric model has three determinants; according to Anders, Farris, & Hutchison (2007); Gardner (2004); Farris & Hutchison (2003); and Duman & Sawathanon (2009) which are; DOI, DRO and DPO respectively stand for days of inventory, days receivable outstanding and days payable outstanding. C2C = DOI + DRO – DPO In what follows, details about each of these parameters will be provided. i.
Days of Inventory (DOI) = (Inventory/COGS)*365 days
DOI is computed by dividing inventory by cost of goods sold and multiplied by 365 days in a year. Day of inventory therefore is directly linked to inventory turnover which is how many times a company turns its inventory in a given period. To sum up, DOI is the length of the average period that elapses from the purchase of materials from suppliers to the sales of corresponding items to customers. As this metric is added up in the calculated of C2C cycle, it may intuitively be said that the lower it is the better for the firm trimming inventory which will ring about a lower DOI, yet too low a DOI may mean that the firm is compromising its service levels. ii.
Days receivable Outstanding (DRO) = (Account Receivable/sale)*365
DRO is computed by dividing accounts receivables by Net sales, and then multiplied by 365 days in a year. DRO relates directly to account receivable turnover which reveals how many times customers are invoiced and the payment received from them are recorded on the income statement during the period in question. In simple term, DRO is the average that elapses from the issuance of a bill to a customer up to the collection of the corresponding cash from the customer.
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Firms naturally would like to be paid on the spot for their products/services. And some retailers typically collect their payments quickly and hence have hardly any outstanding receivables. They are thereby, at an advantage in terms of this metric. And some are paid before selling the relevant product or rendering the relevant service. iii.
Days Payable Outstanding (DPO) = (Accounts Payable/COGS)*365
DPO is computed by dividing the accounts payable by cost of goods sold, then multiplied by 365 days in a year. Thus, it has linkage to account payable turnover which measures how many times a full cycle of been billed by suppliers and making corresponding payments to those suppliers is completed in a given period. DPO in rather simple terms is the average number of days that elapse from the purchase of materials up to making the relevant payment in cash. It is the only component that assumes a minus sign in the calculation of C2C metric. Thus, one can intuitively argue that the higher it is, the better it is for the firm. The result of C2C metric assumes both positive and negative values, although the latter is less common. A positive value indicates the length of time that cash remains tied up in inventory and receivables until payment firm customers. A negative number may arise either from low C2C cycle which suggests that the company is efficient in managing its cash flows, because it turnover its working capital more frequently. 2.4
The Operating Cycle Concept
According to Akinsulire (2011) and Pandey (2008), the operating cycle is the length of time it takes to acquire inventory of raw materials, convert them to finished products, sell them and collect cash from sales. Pandey (2008) posited that operating cycle is the amount of time it takes for a company to turn cash used to purchase inventory into cash once again. This 18
number is calculated by adding the age of inventory (the number of days that inventory is held prior to sale) with the collection period (the number of days required to collect receivables). A company with a short operating cycle is able to quickly recover its investment, while a company with a long operating cycle will have less cash available to meet any short term needs, which can results in increased borrowing and interest expenses. It has been argued that the flow concept of liquidity can be developed extending the static balance sheet analysis of potential liquidation value coverage to include income statement measures of a firms operating activity. In particular, incorporating accounts receivable and inventory turnover measures into an operating cycle concept provide more appropriate view of liquidity management than does reliance on the current and acid-test ratio indicators of solvency. These additional liquidity measures explicitly recognize that the life expectancy of some working capital components depend upon the extent to which three basic activitiesproductions, distribution (sales) and collection are instantaneous and unsynchronized. 2.5.1 The Concept of Corporate Profitability Both liquidity and Profitability are the core concern of the company‟s management. Also, Profitability is expected to have significant impact on company‟s C2C cycle. It might have both positive and negative effect on the company Profitability, for instance, while a company with long C2C cycle might have higher sales because of long credit term given to trade credit customers, high cost of investment in working capital might decrease Profitability as well (Deloof, 2003). Rimo & Panbunyuen (2010) provide the evidence that there are significant negative relationship between Working Capital Management measured by net trade cycle and Profitability which point out that market share leads to the bargaining power with suppliers and customers to shorter the net trade cycle and higher Profitability. Similarly, Lazaridis & 19
Tryfonidis (2006) find the negative relationship between C2C cycle and Profitability measured by gross operating profit. The researchers explain this negative result as shorter C2C cycle will generate more profit for a company. Eljelly (2004), also reports significant negative relationship between the liquidity level and Profitability in companies with long cash conversion. On the contrary, Jeng-Ren, Li, & Han-Wen (2006) find the significant positive relation between the net liquid balance as a measure of Working Capital Management and firm performance measured by return on assets. They find that high profit companies tend to have more working capital balance as a result of using conservative policy. In addition, the result with another measurement, working capital requirement, point out the positive relation which suggest that companies have inefficient Working Capital Management which leads to high account receivable and inventory balance. 2.5.2 Measures of Corporate Profitability
A company should earn profit to survive and grow over a long period of time. Profits are essential, but all management decisions should not be profit centred at the expense of the concerns for customers, employees, suppliers or social consequences. Profit is the difference between revenues and expenses over a period of time (usually one year). Profit is the ultimate „output‟ of a company, and it will have no future if it fails to make sufficient profits. The Profitability ratios are calculated to measure the operating efficiency of the company. Some of the Profitability ratios include the following: Return on Investment (ROI): The term investment refers to total assets or net assets. The fund employed in net assets is known as capital employed. Net assets equal to net fixed assets plus current assets minus current liabilities excluding bank loan. The conventional approach of calculating return on investment is to divide profit after tax (PAT) by investment. 20
Investment refers to pool of funds supplied by shareholders and lenders, while PAT represents residue income of shareholders. Return on Equity (ROE): Common or ordinary shareholders are entitled to the residue profits. The rate of dividends is not fixed; the earnings may be distributed to shareholders or retained in the business. Nevertheless, the net profit after tax represents their return. A return on shareholders' equity is calculated to see the Profitability of owners‟ investment. The shareholders‟ equity or net worth will include paid up share capital, share premium and reserves and surplus less accumulated losses. Net worth can also be found by subtracting total liabilities from the total assets. The ROI is net profit after taxes divided by shareholders‟ equity which is given by net worth. Return on Assets (ROA): Return on Assets expresses the net income earned by a company as a percentage of the total assets available for use by that company. ROA suggests that companies with higher amounts of assets should be able to earn higher levels of income. ROA measures management‟s ability to earn a return on the firm‟s resources (assets). The income amount used in this computation is income before the deduction of interest expense, since interest is the return to creditors of the resources that they provide the firm. The resulting adjusted income amount is the income before any distribution to those who provided funds to the company. ROA is computed by dividing net income plus interest expense by the company‟s average investment in asset during the year. Return on Assets (ROA) is a widely used financial tool to determine the level and intensity of returns that a firm has generated by employing its total assets. Firms are usually considered well off when they generate returns that can attract further investors and lenders, and in trouble if they need to raise the finance required for growth or capital needs, or if their ROA does not convince financiers (Ali, 2011).
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2.6
Conglomerate Business
Conglomerate, in business, a corporation formed by the acquisition by one firm of several others, each of which is engaged in an activity that generally differs from that of the original. The management of such a corporation may wish to diversify its field of operations for a number of reasons: making additional use of existing plant facilities, improving its marketing position with a broader range of products, or decreasing the inherent risk in depending on the demand for a single product. There may also be financial advantages to be gained from the reorganization of other companies (Tsui, 2012).
A conglomerate is a combination of two or more corporations engaged in entirely different businesses that fall under one corporate group, usually involving a parent company and many subsidiaries. Often, a conglomerate is a multi-industry company. Conglomerates are often large and multinational. A corporation that is made up of a number of different, seemingly unrelated businesses.
In a conglomerate, one company owns a controlling stake in a number of smaller companies, which conduct business separately. Each of a conglomerate's subsidiary businesses runs independently of the other business divisions, but the subsidiaries' management reports to senior management at the parent company. The largest conglomerates diversify business risk by participating in a number of different markets, although some conglomerates elect to participate in a single industry (Bligh, 2006).
According to McDonald & Wasko, (2010), there are two philosophies guiding many conglomerates either by participating in a number of unrelated businesses, the parent corporation is able to reduce costs by using fewer resources, or by diversifying business interests, the risks inherent in operating in a single market are mitigated.
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A strategy of diversification spurred the formation of many conglomerates in the mid-20th century, especially as firms sought to acquire unrelated companies whose products and services might better withstand economic slowdowns. In that era, a holding company such as the former ITT Corporation or Gulf + Western might have had interests that included hotels, film studios, telephone service, and insurance. By the late 20th and early 21st centuries, however, global competition created conditions that favoured industry consolidation, as evidenced
by
mergers
among
large
corporations
in
the
banking,
automotive,
telecommunications, computer, retail, and entertainment industries (Bligh, 2006).
History has shown that conglomerates can become so diversified and complicated that they are too difficult to manage efficiently. In the late 19th century many American conglomerates, such as the Standard Oil Company and Trust, sought to control all aspects relating to the development, production, marketing, and delivery of their products (McDonald & Wasko, 2010).
2.7
Empirical Studies on Working Capital Management and Profitability
Many researchers have studied the impact of Working Capital Management on Profitability in both Nigeria and other part of the world. 2.7.1 Nigerian Empirical Studies on Working Capital Management and Profitability In Nigeria, Falope & Ajilore (2009) aimed to determine the effect of WCM on Profitability performance using panel data of the sample of non-financial Nigerian firms for the period 1996-2005. They found a negative relation between operating Profitability and the number of days of inventories, account payables and account receivables for a sample of fifty Nigerian firms listed on the Nigerian Stock Exchange. They also found that there is no significant
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difference between small and larger firms, so both can enhance profit by shortening their C2C cycle. However, Owolabi & Alayemi (2010) study the effects of Working Capital Management as a financial strategy on liquidity as well as Profitability of the firm in Nestle Nigeria Plc for a period of five years from 2004-2009. The results showed that there is a negative correlation between current ratio and Profitability. This means that as current ratio reduces, Profitability of the firm will increase. On the other hand the collection days were regressed against ROCE, which showed that there is negative correlation between collection days and ROCE, which indicates that as collection days are reduced there will be increase in Profitability. It is suggested that the firm should be aggressive in the management of its working capital to improve Profitability. In another Nigerian study, Abdulrasheed, Khadijat, Sulu & Olanrewaju (2011) assessed inventory management in selected small businesses in Kwara State, Nigeria. Using a regression model to explain the effect of inventory value on performance proxy by profit over a period of ten years, the study revealed that a Naira change in stock would cause almost a Naira (92 Kobo) change in Profitability of selected businesses. This result indicated a strong positive relationship between inventory and Profitability of small businesses in Kwara State of Nigeria. They thus, concluded that small businesses are likely to generate higher profit if an effective inventory management is put in place. Ogundipe, Idowu, & Ogundipe, (2012) sampled fifty-four quoted non-financial Nigerian firms for the period 1995 – 2009. Employing correlation and multiple regression techniques, they examined the impact of Working Capital Management on firms‟ performance and market value. The study confirmed that there is a significant relationship between Market valuation, Profitability and working capital components. Specifically, results show that there
24
is a significant negative relationship between C2C cycle, and market valuation and firm‟s performance. Reduction in the length of the C2C cycle therefore will lead to realization of profit maximization objective and consequently, the firm‟s market value. It also found that debt ratio is positively related to market valuation and negatively related to firm‟s performance. Onwumere, Ibe, & Ugbam, (2012) investigated the impact of working capital policies of Nigerian firms on Profitability for the period, 2004-2008, adopting the aggressive investment working capital policies and aggressive financing policies as independent variables and return on assets as dependent variable and controlling for size and leverage, the study revealed that aggressive investment working capital policies of Nigerian firms have a positive significant impact on Profitability while aggressive financing policies have a positive non-significant impact on Profitability. Owolabi & Alu (2012) examined the existence of firms and ensuring the going concern gross depend on liquidity and Working Capital Management of Selected Quoted Manufacturing Companies in Nigeria, adopting Ex-post facto research involving trend analysis of five years financial statements of five manufacturing companies was carried out using purposive sampling technique by means of Multivariate analyses. The result indicated that each Working Capital component affected the company‟s level of Profitability at varying rates, but, these effects when pooled together are not significant. It was recommended that the companies should adequately plan and control their operations, adjust the shortfalls as noted, consider the principles of finance in their decision making, employ the services of experts (analysts) in complex business areas, and conduct periodic stock taking if possible every two weeks.
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Quayyum (2012) surveys the relationship between Working Capital Management and Profitability in Context of Manufacturing Industries in Bangladesh covering a time period of 2005 to 2009, utilizing multiple regressions presents that with exception of food industry all other selected industries have a significant level of relationship between the Profitability Indices and various Working Capital Components. For the Conglomerate industry, inventory turnover period and C2C cycle have a negative relationship with return on asset. The result clearly states that the shorter C2C cycle, the more profitable the firm is likely to be. The firms should also put much importance on their receivables management and payables management to derive the best out of their Profitability. Adegbie (2012), assessed the significant relationship between Working Capital Management and Profitability in a profit making organization with a view to resolving distress conflicts of the manufacturing companies in Nigeria. The results showed that there is a strong relationship between working capital management, conflict and Profitability. The recommendations are, professionals are needed for effective management of working capital in a profit making organization to enhance Profitability, and avoid e- business conflict. A good credit policy should always be in place to guarantee high turnover of debts. More recently, Samson, Josiah, Yemisi, and Erekpitan (2012) investigated the impact of Working Capital Management on the Profitability of 30 sampled Nigerian small and medium sized firms covering the year 2009. Using multiple regression analysis, the results suggest that managers can create value by increasing their firms‟ inventories and receivables turnover. Similarly, a shorter C2C cycle improves the firm‟s Profitability. Uremadu, Egbide, & Enyi (2012) presented empirical evidence of the effect of Working Capital Management and liquidity on corporate profits of quoted Firms in Nigeria Evidence from the Productive Sector using a cross-sectional time series data for the period 2005-2006,
26
using descriptive statistics and OLS methodology. The authors find a positive effect of inventory conversion period (ICP), debtors collection period (DCP); and a negative effect of cash conversion period (CCP), creditors payment period (CPP), on Return on Assets (a mirror of Corporate Profitability). Takon (2013) investigated the impact of C2C cycle on Return on Assets (ROA) of selected Nigerian 46 quoted firms for the period, 2000-2009. Multiple regression technique was used in analyzing the models for testing the hypothesis. The results showed that C2C cycle had a significant negative relationship with Profitability (ROA). Based on the findings, the study recommends that, firms should always reduce the number of days in C2C cycle in order to increase Profitability as to create value for shareholders. 2.7.2 Other Countries Studies on Working Capital Management and Profitability In the Studies in other parts of the world, many scholar had made a contribution empirically on the Working Capital Management and Profitability. For instance, Lyroudi & Lazaridis (2000), conducted a study on similar grounds of the Greek food and beverage industry for the period of 1998, and found a positive relationship between the C2C cycle and current and quick ratios, and between the C2C cycle and return on assets (ROA). The profit margin is observed to move positively with C2C cycle, and the latter is found to have no association with leverage ratios. Similarly, Wang (2002), examined the relation between liquidity management and operating performance, liquidity management and corporate value for firms in Japan and Taiwan. He found a negative relation between C2C cycle and Return on Assets (ROA) and between C2C cycle and ROE, but both are sensitive to industry factors. The findings also imply that aggressive liquidity management, e.g. shortening the C2C cycle, improves operating performance. 27
Another effort by Deloof (2003), in Belgium on the relation between Working Capital Management and firm Profitability sampled 1009 non-financial firms, gathered data over the 1992-1996 period; Profitability is measured by gross operating profit divided by total assets minus financial assets, so ROA is not considered as a measure for Profitability. The level of WCM is measured with the C2C cycle, which is in line with the finding of Wang (2002); shortening the C2C cycle is also associated with higher corporate value for both countries. In the same vein, Lazaridis & Tryfonidis (2006) studied the relation between Working Capital Management and corporate Profitability in Greece used a sample consisting of 131 companies listed on the Athens Stock Exchange from the period of 2001-2004, observed a negative relation between Profitability, measured through gross operation profit, and working capital management, measured with the C2C cycle, and also found that account payables are negatively related to Profitability, which is in line with Deloof 2003). Conversely, in Mauritania Padachi (2006) studied the trends in Working Capital Management and its impact on the Profitability of sampled 58 small Mauritian Manufacturing Firms using panel data analysis for a six year period to 2003. Using pooled ordinary least square regression method, the study found no statistically significant relationship. Applying a fixed effect method however, the same study reported insignificant positive association between inventory days and the C2C cycle with Profitability. The relationship between debtors‟ turnover and Profitability was however significant and negative. The study also found an increasing trend in the short-term component of working capital financing. Garcia-Teruel & Martinez-Solano (2007) studied the effect of Working Capital Management on the Profitability of Small and Medium Enterprises (SMEs) in Spain. They used a panel data regression methodology, consisting of 8,872 SMEs covering the period 1996-2002. The results demonstrate that managers can create shareholders value by shortening their firms'
28
number of days accounts receivables and inventories. Also shortening the firm‟s C2C cycle enhances Profitability, which is in line with the above mentioned studies of Deloof (2003), and Wang (2002). They argue that SMEs should particularly be concerned with WCM, because they can create value if they keep their C2C cycle to a reasonable minimum. Raheman & Nasr (2007) investigated the effects of Working Capital Management on the Profitability of the 94 listed companies in the stock exchanges of Pakistan during the period 1999 to 2004. They studied the effect of different variables of working capital including average collection period, inventory turnover, the average payment period and C2C cycle on net operating income of companies and found a strong negative relationship between the ratios of working capital and Profitability of company. In addition, administrators can create value for shareholders by reducing the C2C cycle up to a desirable level. This study confirms the results of the same studies on the relationship between working capital and Profitability. Koumanakos (2008) analyzed the effect of inventory management on firm performance for the period of 2000-2002 using the ICAP database containing financial information on all firms from three sectors. The findings indicated that high level of inventories was associated with the lower rate of return. Samiloglu & Demirgunes (2008) analyzed the effect of Working Capital Management on the firms Profitability of the Turkey Manufacturing Firms listed in the Istanbul Stock Exchange (ISE) for the period of 1998-2007 using the multi regression model for the analysis. Empirical findings showed the results that receivable conversion period, inventory conversion period and leverage affects negatively on the firm Profitability, while growth in sales affects firm Profitability positively. In the same vein, Singh & Pandey (2008) studied the relationship between the WCM and performance of Hindalco Industries for the period of 1990 to 2007 and found a strong relationship between the WCM ratios and firm performance.
29
Afza & Nazir (2009) studied relationship between WCM policies and a firm‟s Profitability by taking 204 non-financial companies listed on Karachi Stock Exchange (KSE) for the time span of 1998-2005. They used regression analysis and found a strong positive association of low Profitability and aggressive WCM and recommended a conservative approach towards WCM and related financing policies. In an attempt to discover a correlation between SCM and finance performances of Korean Companies; Lee, Song, & Lee (2009) used multiple regression as analytical tool and found positive relations between C2C cycle time and the selected performance metrics, while wholesale and retail trade industry has negative correlation between the variables and Return on Assets as hypothesized. According to their analysis results, as DSO and DOI increase while DPO decreases, a car manufacturing firm may result in better performance. They suggested keeping enough inventories for smooth flow of business and not to lose customers and sales. Mathuva (2009) tested 15 years‟ data of 30 Kenyan firms to investigate the relationship between Profitability and management of working capital. Data were analyzed using Pearson and Spearman‟s correlations. The study found a significant positive relationship between profits and inventory turn-over days, and negative relationship between receivable days and Profitability. Furthermore, it was established that there exists a positive relationship between payment period and Profitability which implies that profitable firms delay their payments. From the developed American economy, Nobanee & AlHajjar (2009) examined the relationship between working capital management, corporate performance and operating cash flow based on a sampled 5,802 publicly quoted non-financial American firms. They employed the Generalized Method of Movement System Estimation. The study reveals that debtors‟ collection period, creditors‟ deferral period and the C2C cycle were all significantly
30
negatively associated with returns. The inventory conversion period however had a significant positive impact with returns. Using ordinary least square regression model, Ramachandran & Janakiraman (2009) analyzed the relationship between Working Capital Management efficiency and earnings before interest and tax of the paper industry in India. The study revealed that C2C cycle and inventory days had negative correlation with earnings before interest and tax, while accounts payable days and accounts receivable days related positively with earnings before interest and tax. So also in Malaysia, Zariyawati, Annuar, Taufiq & AbdulRahim (2009) examined the relationship between Working Capital Management and firm Profitability across six different Malaysian economic sectors. Using pooled ordinary least square model, the study found a significant negative relationship between the C2C cycle and Profitability in most economic sectors. Dong & Su (2010), investigated the relation between Profitability and the C2C cycle and components in Vietnam. They used a sample of secondary data on public listed firms on the Vietnam stock market for the period of 2006-2008. They measure firm Profitability through gross operating profit and found that there is a negative relation between the C2C cycle and firm Profitability. Compared to the other studies mentioned here, this study has a significant weakness, which is the shortness of the sample period. Eizadinia & Taki (2010) showed that the full development and advance of Working Capital Management has effective contribution to the creation of corporate value. Data collected in this study is taken from the financial statements of listed companies in Tehran Stock Exchange for the period 2001 to 2007. Key variables used in this analysis are Inventory Turnover, Accounts Receivable Period, Accounts Payable Period and C2C Cycle. The results show that C2C cycle has inverse and significant relationship with return on assets and also
31
high investment in accounts receivable and inventory lead to lower Profitability of the companies. Gill, Biger & Mathur (2010) sampled 88 American firms listed on the New York Stock Exchange over a period of three years. Using least square regression model, the study found significantly negative relationship between accounts receivable and Profitability, and significantly positive relationship between Profitability and the C2C cycle. The association between Profitability and average payment period and inventory turnover was however, found to be positive and insignificant. Mary, John & Laurie (2010) examined the effect of inventory on firms‟ Profitability before and after two catastrophic supply chain disruptions of the September 11, 2001 terrorist attacks and Hurricane Katrina, with the objective of determining whether there is evidence that inventory has been used as a means of developing supply chain resiliency and the stability of any such relationship. Using separate three-year periods surrounding the disruptions, they applied univariate analysis to examine the macro-level effects on firms‟ Profitability, selected growth measures, and inventory levels across manufacturers, wholesalers, and retailers. Utilizing regressions models find that the effect of inventory on firms‟ Profitability and shows a significant decline for manufacturing in the post - September 11 period with no significant change in the post Katrina period. Similarly Raheman, Afza, Qayyum & Bodla (2010), in their study on Working Capital Management and corporate performance of Pakistani manufacturing sector, and using regression analytical tools, found significant negative relationship between Profitability and each of Inventory Turnover and the C2C cycle. However, insignificant negative and positive relationships subsist between Profitability and each of average collection and payment periods respectively.
32
Rezazadeh & Heidarian (2010) in their study investigated the effect of Working Capital Management on the Profitability of Iranian companies. For this purpose, samples of Iranian listed companies in Tehran Stock Exchange during the period 1997 to 2007 were studied and from these companies, 1356 companies were collected and analyzed as data. The results show that management can create value for company by reducing the amount of inventory and the number of days in collection period. In addition, by making short the C2C cycle also can improve the Profitability of the companies. Rimo & Panbunyuen (2010) investigated the effect of company characteristics on the Working Capital Management in Swedish listed companies by employing quantitative method. The sampled 40 companies in the large capital investment segment listed on NASDAQ OMX Stockholm Exchange with 2007 and 2008 financial data using regression analysis, their results indicate that there is a significant positive association between Profitability and the C2C cycle. Considering the component of the C2C cycle, the regression result point out a significant positive relation between number of days inventory and Profitability which is opposed to the studies of (Deloof, 2003; Raheman & Nasr, 2007; and Lazaridis & Tryfonidis, 2006). Wongthatsanekorn (2010) study of C2C Cycle Management on Profitability of Private Hospital in Thailand by Regular and Panel Data Regression results show that only the independent variable payable deferral period (AP) is negatively related to Asset Turnover (AT) under the control variables. The rest of the independent variables statically reveal no relationship with AT. On the other hand, the results from panel data regression show that both receivable conversion period (AR), and AP are negatively related with AT. They suggest that the listed firms in SET can increase corporate Profitability by decreasing AR and AP.
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Ali (2011), explored the association between Working Capital Management and the Profitability of textile firms in Pakistan. Using balanced panel dataset covering 160 textile firms for the period 2000–2005 by means of estimate an ordinary least squares model and a fixed effect model. Return on assets is found to be significantly and negatively related to average days receivable, positively related to average days in inventory, and significantly and negatively related to average days payable. Also, return on assets has a significant positive correlation with the C2C cycle, which would suggest that a longer C2C cycle is more profitable in the textiles business. Alipour (2011), in Iran studied the relationship between Working Capital Management within time territory of 2001-2006 and sample 1063 out of 2628 companies using multiple regression and Pearson‟s correlation found a negative significant relation between number of days accounts receivable and Profitability, a negative significant relation between inventory turnover in days and Profitability, a direct significant relation between number of day‟s accounts payables and Profitability and there is a negative significant relation between C2C cycle and Profitability. Hayajneh & Yassine (2011), employed the analytical tools of least squares regressions model to investigate the relationship between working capital efficiency and Profitability on 53 Jordanian manufacturing firms listed on Amman Exchange Market over a seven year period to 2006. The results of the study found a significant negative relationship between Profitability and the average receivable collection period, average inventory conversion period, average payment period and the C2C cycle. The study further revealed a positive significant relationship between the size of the firm, sales growth and current ratio on one side and Profitability on the other side.
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Saghir, Hashmi, & Hussain (2011) used a sample of 60 textile companies listed at Karachi Stock Exchange (KSE) for the period of 2001-2006 in an attempt to establish a relationship that is of statistical significant between Profitability, the C2C cycle and its components (Number of days Accounts receivables, Number of days Accounts payables and Number of days Inventory). They adopted multiple regressions and prove that there is statistically negative significance between Profitability, measured through Return on Asset, and the C2C cycle. Results show significant and negative relationship between ROA and Numbers of days Account receivable, payable turnover in days or Numbers of days Account Payable as well as inventory turnover in days or Numbers of days Inventory. They suggest that managers can create profits for their companies by handling correctly the C2C cycle and keeping Number of days Accounts receivables, Number of days Accounts payables and Number of days Inventory to an optimum level. Sharma & Kumar (2011) studied the effect of WCM on Profitability of Indian firms. They used a sample of 263 non-financial BSE out of 500 firms listed on the Bombay Stock Exchange from 2002 to 2008. They analysed the data by using OLS multiple regression. They found a positive relation between WCM and firm Profitability, although the C2C cycle and ROA relation is not statistically significant. They found that account receivables are also positively related with ROA and that account payables are negatively related to ROA. This means that when Indian firms increase their C2C cycle, Profitability will be higher. The authors argue that this is because India is an emerging market. Firms are seen more profitable if they give their clients more trade credit, therefore they have more clients, which means more sales, will in turn lead to more profit. Suhail & Lahcen (2011) made a case study on 53 Jordanian firms which are listed with Amman Exchange Market and collected a sample data for the years of 2000-2006. The study 2SLS and OLS regression model were applied and the results showed a negative relationship 35
of C2C cycle and its components (number of days account receivables, number of days inventory and number of days account payables) with Profitability of Jordanian‟s firms. And size of the firm has positive significant association with Profitability which means larger size of companies tends to lead increase in Profitability. Ahmadi, Arasi & Garajafary (2012), using correlation and regression as tools of analysis explore the relationship between Working Capital Management and Profitability at companies of food industry group member at Tehran Stock Exchange. 33 companies were selected for a period of five years from 2006-2011 using correlation and regression as tools of analysis. The results of their study showed that there is a reverse relationship between the variables of Working Capital Management and Profitability. It is also found out that increasing collection cycle, debt payment period, inventory turnover and C2C cycle leads to decreasing Profitability in the companies. According to the research, managers can create a positive value for stockholders by decreasing collection cycle, debt payment period, inventory turnover and C2C cycle to the lowest possible level. Ani, Okwo, & Ugwunta (2012) expanded the horizon of knowledge in this area by shedding more light on Working Capital Management as measured by the C2C cycle, and how the individual components of the C2C cycle influence the Profitability of world leading beer brewery firms, Multiple regression equations were applied to a cross sectional time series data of five world leading beer brewery firms. Their empirical results show that the relationship between world leading firms‟ C2C cycle, sales growth rate and Profitability is positive and therefore, that C2C cycle and sales growth rate are effective determinants of the sector‟s Profitability. This result is strengthened by the multiple regressions which confirm statistically that the C2C cycle and sales growth rate significantly impacts on the world top five leading beer brewing companies‟ Profitability.
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Attari & Raza (2012) looked into the association of the C2C cycle with the size and Profitability of the firms in the four specific manufacturing sectors listed at Karachi Stock Exchange 31 sampled firms out of the total firms in the related sectors i.e. 143 covering the period of 2006-2010. The data analysis was conducted using One-Way ANOVA and Pearson Correlation Techniques and found a negative correlation between C2C cycle and Profitability in terms of return on total assets Charitou, Elfani, & Lois (2012) empirically investigated the effect of Working Capital Management on firm‟s financial performance in an emerging market. They used data set of firms listed in the Cyprus Stock Exchange for the period 1998-2007. Using multivariate regression analysis of their results indicate that the C2C cycle and all its major components; namely, days in inventory, days sales outstanding and creditors‟ payment period are associated with the firm‟s Profitability. 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 collapses of giant organizations worldwide. Karadagli (2012) focuses on the effects of Working Capital Management as measured by C2C cycle and net trade cycle on the firm performance for a sample of Turkish listed companies and searches for potential differences between the Profitability effects of Working Capital Management for the SMEs and for the bigger companies with an accompanying aim to examine whether net trade cycle can efficiently substitute for C2C cycle as a measure of Working Capital Management employing the data for the period of 2002-2010 by using pooled panel regression analysis and found that an increase in both the C2C cycle and the net trade cycle improves firm performance in terms of both the operating income and the stock market return for SMEs. Whereas, for bigger companies a decrease in C2C cycle and net trade cycle is associated with enhanced Profitability. 37
Napompech (2012) examined the effects of Working Capital Management on Profitability using regression analysis based on a panel sample of 255 companies listed on the Stock Exchange of Thailand from 2007 through 2009. The results revealed a negative relationship between the gross operating profits and inventory conversion period and the receivables collection period. Therefore, suggesting that managers can increase the Profitability of their firms by shortening the C2C cycle, Inventory Conversion Period, and Receivables Collection Period. However, they cannot increase Profitability by lengthening the payables deferral period. Ray (2012) evaluated the Impact of Working Capital Management Components on Corporate Profitability: Evidence from Indian Manufacturing Firms using a sample of 311 Indian manufacturing firms for a period of 14 years from 1996-97 to 2009-10 by means of multiple regressions. The result suggests a strong negative relationship between the measures of Working Capital Management including the number of days accounts receivable and C2C cycle, financial debt ratio with corporate Profitability. He suggested that further research be conducted on the same topic with different companies and extending the years of the sample. Soekhoe (2012), tried to investigate the linkage between the Working Capital Management and Dutch Firm‟s Profitability by using a sample of 70 firms of different sectors for the duration 2006 to 2010. He used pooled and fixed effect model of regression analysis and descriptive statistics with correlation analysis. All of these models‟ results shown that C2C cycle positively correlated with Profitability and inventory conversion period have positive significant relationship with Profitability which indicates higher level of inventory leads to increase in profits. However, account receivables in days and account payable in days have negative and significant association with Profitability it means those firms which pay to their supplier quicker and have a longer period to collect receivables from their customers, faces the reduction in profits. 38
Usama (2012) aims to extend the Rehman and Nasr finding regarding Working Capital Management and its affect on Profitability and liquidity of Pakistani firms. For that purpose he selected the other food sector and selected the data from 2006-2010 of 18 companies of this sector listed on Karachi Stock Exchange using multiple Regression and found that there is negative relationship between net operating Profitability and inventory turnover in days, average collection period and C2C cycle. Average payment period is also negatively correlated. Alavinasab & Davoudi (2013) examined the relationship between Working Capital Management and Profitability for listed companies on Tehran stock exchange. 147 companies were selected for the period of 2005-2009. They used Multivariate regression and Pearson correlation to find that a negative significant relationship exist between C2C cycle and return on assets and there is also a negative significant relationship between C2C cycle and return on equity, and recommended that Companies in order to improve their operation and increase in shareholder‟s wealth, must adopt policies and plans to reduce number of day accounts receivable. Anser & Malik (2013) assessed the effect of C2C cycle on Firms‟ Profitability of Listed Manufacturing Companies of Pakistan by taking into consideration 5 years financial statements data starting from 2007 to 2011. Regression results showed that C2C cycle is having significantly inverse association with both return on assets and equity indicating that lesser the C2C cycle greater would be the Profitability measured through return on assets and equity. Therefore suggest that the receivable collection period and inventory selling period must be reduced along with the extension of payment period to increase the Profitability of manufacturing sector organizations.
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Leeper & Chambers (2013) conducted study in Pakistan also with the aim of looking into the association of the C2C cycle with the size and Profitability of the firms in the Automobile and Parts, Conglomerate, Chemical, and Food Producers of manufacturing sectors listed at Karachi Stock Exchange. They sampled 31 firms out of the total firms in the related sectors of 143 covering the period of 2006 -2010 by using One -Way ANOVA and Pearson correlation techniques. And find a significant positive relationship between the length of C2C cycle and the Profitability of firms in terms of return on total assets Majeed, Makki, Saleem, & Aziz (2013) examined the impact of C2C cycle on the performance of Pakistani manufacturing firms. The study used the sample of 32 companies selected randomly from three manufacturing sectors i.e. chemical, automobiles, and construction & material for the period of five years ranging from 2006 to 2010. The correlation and regression analysis were used and the study found that the average collection period of accounts receivables, inventory conversion period and C2C cycle have negative relationship with firm‟s performance. Regarding the average days of accounts payable, previous studies reported negative correlation of this variable and the Profitability of the firm. Panigrahi (2013) attempted to study in depth the inventory management practices of Indian Conglomerate companies and its impact on Working Capital efficiency for a sample of five top Indian Conglomerate companies over a period of ten years from 2001-2010. This study employs Regression analysis and found that there is a significant negative linear relationship between inventory conversion period and Profitability. Pouraghajan, Rekabdarkolaei, & Shafie (2013) Investigated the Effects of Working Capital Management and Capital Structure on Profitability and Return on Assets in Iran by sampling listed automotive companies in the Tehran Stock Exchange covering 2006 to 2010 using
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regression analysis showed that inventory turnover and C2C cycle have significant and negative effect on the returns on assets. Shah & Chaudhry (2013) attempted to investigate the relationship between C2C cycle and Profitability in Pakistani textile sector using data from 20 listed firms in Karachi stock exchange for the period of 2001-2011 using the techniques of correlation coefficient and regression analysis have found a significant relationship between net operating Profitability and the average collection period, average payment period and C2C cycle. Warnes (2013) examined the impact of Working Capital Management on the Profitability over the period of five years from 2007-2011 by utilizing the data of Conglomerate manufacturing firms listed at Karachi Stock Exchange (KSE). Multiple regression models are applied and the findings of the study validated a negative relationship between determinants of Working Capital Management and Profitability of Conglomerate manufacturing firms. Number of days inventory (DINV) significantly and positively impacted on Return on Asset (ROA). C2C cycle also has positive and significant impact on ROA that mean reduction in C2C cycle will lead to increase the profit of the firms. ROA regression model shows that Account payable in days (DAP) has significant and negative impact on ROA of the firms. Results suggest that by reducing the period of C2C cycle at a certain level, Profitability of cement manufacturing firms can be increased. Concisely, the findings of these studies reviewed reveal diverse outcome, one group is of the view that Working Capital Management is inversely related with firm Profitability, whereas, another group is of the view that Working Capital Management is positively with firm Profitability. Likewise, some studies revealed that Working Capital Management has significant influence on firm Profitability, while, on the other hand some studies revealed insignificant relationship between Working Capital Management and firm Profitability,
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whereas most of the studies used multiple regressions analysis and the frequent proxy for Profitability is ROA. While Working Capital Management components are: Inventory Days; Account Receivables Days; and Account Payable Days and C2C cycle. 2.8
Theoretical Framework on Working Capital Management
This section discusses theories as well as prior works in which relevant theories to this research topic are used. This research work will be based on cash management theory (monetary theory and financial theory) and cash cycle theory. This is aimed at viewing the relationship that exist between dynamic liquidity measures in cash management and cash cycle theories. 2.8.1 Monetary Theory Numerous theories have been evinced to explain the cash management behavior of firms. Almost all of these theories can be generalized into a proposition of the existence of a stable relationship between a few important independent variables and the stock of money demanded. The two basic transaction models most commonly accepted in the financial literature are the deterministic Baumol-Tobin and the stochastic Miller-Orr inventory models. These models are presented in monetary theory and are consistent with the theory of the firm (Raheman & Nasr, 2007). Baumol (1952) as cited in (Raheman & Nasr, 2007) suggested that cash balances could be treated in the same way as inventories of goods. A stock of cash is its holder‟s inventory, and like an inventory of a commodity, cash is held because it can be given up at the appropriate moment, serving as processor‟s part of the bargain in an exchange. The firm is presumed to hold the amount of money, which minimizes the interest cost by holding money rather than investing it in short-term investments and the transaction costs associated with transferring between securities and cash.
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In this framework the firm is assumed to finance its expenditures by selling securities or by borrowing, and the firm has a steady stream of expenditures but has no receipts. In practice, the behavior is more complicated and the cash balances are the result of the imperfect synchronization of expenditures and receipts, which are often uncertain. This uncertainty is included in the stochastic cash management model derived by Miller and Orr in 1966 as citied by (Raheman & Nasr, 2007). This approach permits net cash flows to fluctuate in a completely stochastic way. Unfortunately, this feature is offset by the fact that the model is only capable of dealing with two types of assets – cash and marketable securities – and does not incorporate payables. Both models referred to above, imply that there are economies of scale in the use of money or, equivalently, that the elasticity of the demand for money with respect to transactions is less than one. In these models the scale operator is transactions volume, mostly measured by sales. There are, however, alternative measures presented in the demand for money literature, such as wealth, production, and market capitalization. In their model, Attanasio, Guiso, & Japelli (2002) measured transaction costs with the time costs. The cash manager is assumed to need time to make transactions, and that money is a way of saving on transaction time, and optimal money balances are chosen in order to trade off the time cost of transactions against the cost of holding money instead of an interestbearing asset yielding a nominal return per period. The cash manager chooses money to minimize the sum of the cost of transaction time and forgone interest, subject to a transaction technology. They present behavioral cash management models, such as deterministic and stochastic models as follows: m = (ω A β / R)1/(1+β) c(β + γ)/(1 + β) (2.1) 43
Where m is the real money balances, R is the nominal rate of return, A is a measure of technology improvements, cis the scale operator. The equation is based on an assumption that the cash manager behaves as min τω+ Rm, subject to τ = Acγ(c/m)β (where τω = transaction time, τ = the time cost of transaction ω, and Rm = forgone interest). This equation encompasses several models. By setting γ = 0 and β = 1, one obtains the Baumol-Tobin square root formula. If γ = 0 and β = 2, Equation (2.1) reduces to Miller-Orr solution (Attanasio et al. 2002). The effects of the exogenous factors on the strategic cash management decisions can be examined by transforming Equation (2.1) to the estimation form and testing its stability. In the empirical equation the variable A can be seen as error term as well as index of the state of financial sophistication in the firm (Attanasio et al. 2002). If transaction costs and the value of time of the cash manager are supposed to be constant, the long run model can be presented in its conventional form as follows: (2.2) Where M* is the desired nominal cash balances, A is the fixed transaction cost; α1 and α2 are the elasticity‟s of M* with respect to the value of transactions (T = the scale operator), and the rate of interest (i). (Note the distinct meaning of the notation A in the models 2.1. and 2.2.) 2.8.2 Financial Theory As a representative for the liquidity management, cash management can be linked to financial theory by considering its importance in an imperfect market. This can be done, by adding it to the financial theoretic models, such as the Capital Asset Pricing Model (CAPM) or the Modigliani-Miller (MandM) model. The effects of the inclusion of cash balances in these theoretical models show the importance of liquid assets for the value of a firm (through the
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systematic risk component) and for the optimal capital structure (through the liquidity slack concept). In addition of the reasons for cash balances presented in monetary theory (and accepted in financial theory), financial theory considers some strategic reasons closely related to the Keynesian speculation motive of money. More recently Titman (2002) applied the Modigliani and Miller theorem and studied the effect of financing and risk management on the firm value and impact of suppliers of capital on capital structure choices during capital market imperfection. 2.8.3 Cash Cycle Theory As cash is often the ultimate determinant of company death or survival, the explicit focus on cash management often safeguard for the management of growth and liquidity. Cash flows and managing operating cash cycle are vital components for a company in introductory and rapid growth phase. For a growth company, the degree to which the firm can take advantage of available cash directly determines the self-financeable rate of growth liquidity (Churchill & Mullin, 2001). Moreover cash flows and cash days can be used to concisely demonstrate the effect of (liquidity) in and on business process, in a way that is both meaningful and familiar to people. Churchill & Mullin (2001) model for effective cash management and cash cycle theory relies on three levels that can be “pulled” to affect the self-financeable growth (SFG) rate. They demonstrate how the mechanisms allow for an intuitive and respective method for releasing cash for growth and monitoring performance for survival. Firstly, affecting the duration that cash is tied up in the operating cash cycle (OCC) by example decreasing inventories will release cash as will stricter account receivable policies. The absolute amount of cash tied for the said duration constitutes the second lever.
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Thirdly, the income level of how much cash is generated from sales and other activity during a cycle is considered. The business process component of the proposed model is based on the overall process. The PDM category is concerned with solution creation, 5cm with acquiring the required inputs and CRM with identifying acquiring and retaining customers. To illustrate the use of the model and concept behind it, a company facing a set of interlinked barriers to growth and liquidity may be considered. For example, to develop their supplier network ties, the management decides to hire a new sourcing agent, for this, they will need cash. Ways to release cash from OCC into growth and liquidity may be from persuading existing network partners to renegotiate terms on accounts payable, or selling off inventory. And the urgency of the matter is a determinant. Similarly, a newly hired sourcing agent may be able to shift component stocking to the suppliers, releasing cash from OCC by a relational marketing exchange. Ganesan (2007), hypothesized that firms with more debt hold more cash (generated from sales) able to service it, and that firms simultaneously allocated some of the extra cash savings and some to debt payments (to suppliers). Working capital, acquisitions and capital expenditures work in the same way as expected. Less Profitability firm hold less cash, showing that an importance source of liquid assets is the current cash flow. Moreover, uncertainty expressed through higher volatility of both cash flow and cash conversion forces firm to hold more cash. The length of the C2C cycle, the dividend dummy and the ratio of long term debt to total debt are not significant. Conversely, intangibles have a negative impact on cash showing that they may affect cash at the firm level through other channels than assets specificity. For example, a high value of goodwill (included in intangibles) is a sign that the firm made a string acquisition in the past, depleting its intend sources. There is
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strong evidence that the specificity of assets in an industry affects the propensity of firms to hold cash. In a nutshell, the study identifies theories that were found relevant to the research such as monetary theory, financial theory and cash cycle theory, where the monetary theory further identifies two basic cash management models of deterministic Baumol-Tobin and Stochastic Miller-Orr Inventory models that explains demand for money motive of maintaining cash. Financial Theory identifies CAPM Model that explains the risk and cost of holding cash, while the cash cycle theory explains the relationship between working capital, operating cycle, C2C metric and its components in managing liquidity. From the foregoing therefore, the researcher is of the opinion that among the various theories that explain C2C strategies (metric), monetary, financial, and cash-cycle theories were the theories that best explain this research work. These three theories when applied to this research view the relationship in terms of cash (Liquid Asset) and Liquidity Management (Monetary Theory) Cash Management and Liquidity (Financial Theory) and among dynamic liquidity indicators; days of inventory, days receivable outstanding, days payable outstanding, C2C metric and cash management (cash-cycle theory).
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CHAPTER THREE RESEARCH METHODOLOGY 3.1
Introduction
This section explains the research design and other methodological issues involved in this study. The population of the study has been presented, as well as the sampling technique and the sample size of the study. The dependent and the independent variables are also presented and their measurements explained. Lastly, methods of data collection and the techniques of data analysis are also explained.
3.2
Research Design
This study is carried out based on historical panel data analysis. The data is analysed with a view to establishing relationship between the study variables. This makes the ex post factor research design suitable for the study where the variables of the study were not restricted as the event of the study has already happened. The Ex-post facto research design considers a research problem in which the independent variables have already occurred and the researcher starts with the observation of a dependent variable in retrospective for their possible relations to, and effects on the independent variable(s). In this study, the ex-post facto design is used to establish causal relationship between Working Capital Management and Profitability of listed Nigerian Conglomerate companies, that is, to measure the extent to which Working Capital Management is associated with the Profitability of listed Nigerian Conglomerate Companies and its effects. In this study, the ex-post facto design is used to establish causal relationship between corporate Profitability and the three variables that determine the net operating cycle, namely: inventory conversion period, debtors‟ conversion period and payables deferral period as well as the C2C cycle. The researcher believes that this design is adequate and appropriate for the measurement of the impact of Working Capital Management on the Profitability of listed Nigerian Conglomerate Companies. 48
3.3
Population of the Study
The population of the study consists of six Conglomerate companies listed on the Nigerian Stock Exchange. The annual reports and accounts of Conglomerate quoted companies are purely accessible. The annual reports are the primary sources of data for this study. Table 3.1 presents the six companies that make up the population of the study: Table 3.1: Population of the Study. S/N. Company
Date Listing
1 2 3 4 5
A.G Leventis Nigeria Plc Chellarams Plc John Holt Plc SCOA Nigeria Plc Transnational Corporation of Nigeria Plc (TransCorp) 6 UAC of Nigeria Plc Source: NSE Fact book 2012
of Paid Up Capital (N)
1978 1977 1974 1977 2006
1,323,645,000.00 361,463,000.00 195,000,000.00 324,737,000.00 12,906,999,000.00
1974
800,360,000.00
The study covered the listed Conglomerate companies operating in the NSE as at 31/12/2012 as presented in Table 3.1. For any company to be included in the working population it must be quoted on the NSE on or before 31/12/2002 and it must have complete annual report and account. This will be done in order to avoid the problem of missing data as the financial reports of unlisted companies are not publicly available. Only Transnational Corporation of Nigeria Plc which was listed on the Nigerian Stock Exchange in the year 2006 failed to meet the above criteria. It is therefore the only company that had been excluded from the study population on the ground that it lacks the required data to cover the period of the study. The five remaining Conglomerate Companies, namely, A.G Leventis Nigeria Plc, Chellarams Plc, John Holt Plc, SCOA Nigeria Plc, and UAC of Nigeria Plc will form the working population for this study. The working population of firms is listed in Table 3.2 along with dates they were listed in the NSE.
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Table 3.2: Working Population. S/N. Company 1 A.G Leventis Nigeria Plc 2 Chellarams Plc 3 John Holt Plc 4 SCOA Nigeria Plc 5 UAC of Nigeria Plc Source: NSE Fact book 2012 3.4
Date of Listing
Paid Up Capital
1978 1977 1974 1977 1974
1,323,645,000.00 361,463,000.00 195,000,000.00 324,737,000.00 800,360,000.00
Sample Size
As the size of the new population for this research is not large and the researcher is confident that studying all the elements in the population will not be out of place, all the five (5) firms that emerged are taken as the working population of the study as presented in Table 3.2, these are A.G Leventis Nigeria Plc, Chellarams Plc, John Holt Plc, SCOA Nigeria Plc, and UAC of Nigeria Plc. 3.5
Sources and Methods of Data Collection
In conducting this study, documentary evidences from secondary sources are used. The documentary accounting data were obtained primarily from the Fact-book maintained by the Nigerian Stock Exchange (NSE) and the published annual accounts and reports of the studied Firms. This method of data collection is also known as non-survey. Data collected from these sources were used for the computations of the ratios that are used to measure the Working Capital Management and Profitability of the listed Nigerian Conglomerate companies. The data covered such items as turnover, cost of sales, profit before and after tax, total assets, trade debtor, trade creditor, stocks, fixed asset, current asset, current liability, and long-term liability. 3.6
Variables of the Study
The variables of this study consist of Dependent and Explanatory Variables.
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3.6.1 Dependent Variables For the purpose of this study, the dependent variable, companies‟ Profitability is measured by Return on Assets (ROA), which is defined as profit before interest and tax divided by total assets. This is in conformity with the works of Falope & Ajilore (2009) and Afza & Nazir (2009). The before tax net income is adopted due to the fact that taxes are charged at fixed rates of assessable income and not normally controllable by management. 3.6.2 Explanatory Variables The explanatory variables consist of independent and control variables. The independent variables of average collection and payment periods, and inventory turnover as measures of working capital management, which were used in previous studies of Padachi (2006), Sha and Sana (2006), Falope & Ajilore (2009) and Gill, Biger & Mathur (2010), Hayajneh & Yassine (2011), with Raheman & Nasr (2007) and Zariyawati, Annuar, Taufiq & Abdul Rahim (2009), these are basically the key variables that influence working capital. The independent variables have been computed as follows: Inventory Turnover Period (ITP): measures the number of days inventory is held by the company before it is sold. The less number of days sales in inventory indicate that inventory does not remain in warehouses or on shelves but rather turns over rapidly from the time of acquisition to sale (Raheman & Nasr, 2007). This ratio is measured as follows: ITP = Average inventory X 365 Cost of Sales Average Collection Period (ACP): measures the number of days it takes to collect cash from debtors. Days sales in receivables measure the effectiveness of the firm‟s credit policy. It indicates the level of investment in receivables needed to maintain the firm‟s sales level and is measured as follows:
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ACP= Average debtors X 365 Sales Average Payment Period (APP): measures the number of days a firm takes to pay its suppliers. Thus, this ratio represents an important source of financing for operating activities (Falope & Ajilore (2009) and Gill, Biger & Mathur (2010)). The ratio is measured as follows: APP = Average creditors X 365 Cost of sales Cash-to-Cash cycle (C2C): measure to gauge Profitability. It measures the net time interval between actual cash expenditures on a firm‟s purchase of productive resources and the ultimate recovery of cash receipts from product sales (Falope & Ajilore, (2009) and Gill, Biger & Mathur (2010)). It is measured as follows: C2C = (Inventory turnover+ Average collection) - Average payment These are in line with Padachi (2006); Sha and Sana (2006); Falope & Ajilore (2009); Gill, Biger & Mathur (2010); Hayajneh & Yassine (2011); Raheman & Nasr (2007); and Zariyawati, Annuar, Taufiq & Abdul Rahim (2009) In order to have an appropriate analysis of the impact of Working Capital Management on the Profitability of firms, different studies have incorporated the use of other variables which are theoretically suggested to affect firm Profitability. Along the same line the present study will, in addition to the Working Capital Management variables, will take into consideration two control variables relating to the companies. The measure of the logarithms of total sales of the companies will be adopted for size as one of the control variables. This measure was used in Raheman & Nasr (2007), Dong & Su (2010) and Gill, Biger & Mathur (2010). Similarly, consistent with the works of Raheman & Nasr (2007), Zariyawati, Annuar, Taufiq & Abdul Rahim (2009), Dong & Su (2010) and Gill, Biger & Mathur (2010), leverage as measured by the ratio of total debt to total assets will be added as the second control variable. 52
3.7
Method of Data Analysis
In analyzing the relationship between the Working Capital Management measures and Profitability, the study employs the statistical tools of Descriptive Statistics, Pearson Correlation Coefficient, Fixed-Effect and Random-Effect (GLS) Regression. 3.7.1 Descriptive Statistics Descriptive statistics is used in this study to compute the summary statistics that describe the central tendency, as well as how the data spread out around this value, or the variability. This tool is used to describe the dependent and the independent variables of the study by computing the mean, median, mode range and the standard deviation of the variables. 3.7.2 Pearson Correlation Pearson Correlation analysis is particularly useful in ascertaining the strength and direction of association between variables one-on-one. This is used by the researcher to determine the nature of relationship between all variables under study so as to understand their individual relationship with one another before regressing them. The value ranges from -1 to +1, but specifically there are four logic behind correlation coefficient which are: i.
If the variables are independent i.e. there is no relationship between them the correlation coefficient will be zero (0).
ii.
If the variables exhibit a perfect positive relationship i.e. +1 the variables are positively related, which means as one variable changes the other changes with the same proportion and direction.
iii.
If variables show a perfect negative relationship i.e. when correlation is equal to 1, it means as one variable increases the other decreases by the same proportion.
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iv.
If correlation coefficient is between positive perfect relationship (+1) and a perfect negative (-1), the closeness of the direct or inverse relationship between the relationship of the variables determines the extent of their correlation.
3.7.3 Multiple Regression Techniques In an attempt to determine the variations in dependent variable (Profitability) due to variation in any of the independent variables (i.e. Inventory Turnover Period (ITP), Account Collectible Period (ACP), Account Payable Period (APP) and Cash-to-Cash (C2C)), the researcher used multiple regression analysis. This is because multiple-regression is expected to explain the variation in dependent variable due to variation in any of the independent variables. However, the selection of the appropriate statistical techniques among the many multiple statistical tools that were available will surely depend on the measurement of the research variables. Fixed-Effect Regression and Random-Effect (GLS) Regression: A classical test for the panel data is one of Random Effect Model versus Fixed Effect Model. This analysis is employed in the conduct of this study. This technique of data analysis is used to examine the relationship between the dependent (ROA) and the independent (Working Capital Management component) variables. It predicts the dependent variable, using the information derived from the analysis of the independent variable. The coefficient of correlation (R) indicates the extent of the relationship between the independent variable and dependent variable in each model. The coefficient of determination (R2) also indicates the extent to which the independent variable explains, in each model, the variability in the dependent variable. Lastly, the coefficient of the independent variable shows the amount of change in the independent variable to have a unit change in the dependent variable.
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Estimating models from panel data requires first, to determine whether there is a correlation between the unobservable heterogeneity ηi of each firm and the explanatory variables of the model. If there is a correlation (fixed effects), we would obtain the consistent estimation by means of the within-group estimator. Otherwise (random effects) a more efficient estimator can be achieved through estimating the equation using Generalized Least Squares (GLS). The normal strategy to determine whether the effects are fixed or random is to use the Hausman specification test under the null hypothesis E(ηi/xit) = 0. If the null hypothesis is rejected, the effects are considered to be fixed, and the model is then estimated by OLS. If the null hypothesis is accepted, we would have random effects, and the model is then estimated by GLS. In this way we achieve a more efficient estimator of β. This technique of data analysis will help in ascertaining the significance of the relationships between Profitability and each of the independent variables. The different elements of the Working Capital Management will be adopted as the independent variables. Each null hypothesis, designed to assess the significance of the relationship between each independent variable and Profitability will be tested using the Fixed-Effect Regression and Random-Effect (GLS) Regression Statistics. The researcher intends to; first, examine the separate impacts of the independent variables before evaluating their overall impact on the dependent variables (DV). The following statistics were used in the regression models to analyze the effect of each individual factor on DV, and to test the utility of the hypotheses. a. Partial slopes (b): These are the multiple regressions coefficient that explain an increase or decrease in the dependent variable as a result of increasing or decreasing the value of the predictor or independent variable by one more unit while holding
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other variables constant. The dependent variable unit increase or decrease depends on whether the partial slope has a positive or negative sign respectively. b. R – Square (R2): This is called the multiple coefficient determination. The R2 of a regression model is the fraction of variation in the dependent variable that is accounted for a capable of being explained by all the independent variables in the regression model. c. t- Statistics: This is the famous student test. It is used in regression model to test the significance of each independent variable in the model. Generally, regression software‟s compare t-statistic, as computed from the data, with students t distribution (i.e. critical value of t) to determine the probability (p-value) of t- statistics. This scenario or technique is used to test hypotheses about independent variables. Thus value of t-statistic is compared with critical value of t at a particular level of significance (e.g 5%). d. Fishers‟ F test: This is called the overall F test. T is used to assess the utility of the regression model by testing the significance of the relationship between the dependent variables in the model. F statistic is used to test the model general null hypothesis: None of the independent variables (x1, x2, x3, x4, ----- xn) is significantly related to the dependent variable (y). The condition for rejecting this null hypothesis is that F (value) > (critical F value) at a specified level of confidence. Note that being a multiple regression model all the above mentioned statistics (a-d) were used on each hypothesis in analyzing the effect of each individual factor on DV, and to test the utility of the hypotheses.
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3.8
Model of the Study
In order to evaluate the strength of the relationship between the Working Capital Management measures on one hand, and Profitability on the other. The study adopts return on net assets (ROA) as the proxy for Profitability. The return on net assets is derived from the Published Annual Reports of the companies under study. The independent variables are equally derived from the same Published Annual Reports. Since other factors apart from the explanatory and identified control variables are likely to affect the companies‟ Profitability, the coefficient of determination (r2) is computed to explain the changes in the dependent variable attributable to the independent variables. All other things that affect the Profitability score is factored into the relationship by adding an error term, ϵ. The functional relationships among these variables are therefore defined as: ROA it = f ( ITP, ACP, APP, C2C, SZ,LEV) it + ϵit From this general form of the regression equation four models, each designed to test one hypothesis is developed. This model is consistent with the works of Padachi (2006), Garcia– Teruel and Martinez-Solano (2007), Falope & Ajilore (2009) and Hayajne & Yassine (2011). ROAit = α0 + α1 ITPit + α2 SZit + α3 LEVit + ϵit
Model 1
ROAit = α0 + α1 ACPit + α2 SZit+ α3 LEVit+ ϵit
Model 2
ROAit = α0 + α1 APPit + α2 SZit+ α3 LEVit+ ϵit
Model 3
ROAit = α0 + α1 C2Cit + α2 SZit+ α3 LEVit+ ϵit
Model 4
Where: ROA denotes Return on Net Assets; α0 represents the fixed intercept element; 57
α1 represents the ratio of change in ROA to a unit change in each substituted explanatory variable; i represents the number of companies of the panel data t represents the time periods of the panel data ITP denotes Inventory Turnover Period; ACP denotes Average Collection Period; APP denotes Average Payment Period; C2C denotes Cash-to-Cash Cycle; SZ denotes Size; α2 represents the ratio of change in ROA to a unit change in Size; LEV denotes Leverage; α3 represents the ratio of change in ROA to a unit change in Leverage; and ϵit is the error term that is factored to satisfy the linear regression model assumption.
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CHAPTER FOUR RESULTS AND DISCUSSION 4.1
Introduction
This chapter presents the analysis made of the documentary data generated and the tests of null hypotheses. The statistical software STATA 12.0 was used to analyse the relationship between the dependent and the independent variables using Pearson Correlation Coefficients and Regression analytical tools.
It also presents the descriptive statistics results which
provide summary statistics for the variables of the study. The correlation coefficients result in an effort to establish the nature of the correlation between the dependent and the independent variables and also to ascertain whether or not multi-collinearity exists as a result of the correlation among variables. 4.2
Descriptive Statistics of the Variables of the Study
Table 4.1 provides summary statistics for the variables of the study. The summary statistics include measures of central tendency, such as the mean, and the measures of dispersion (the spread of the distribution), such as the standard deviation. All the variables were computed from the relevant balance sheets and income statements of the sampled companies. Table 4.1: Descriptive Statistics of the Variables Variables ROA
MEAN
STD DEV
MIN
MAX
4.2348
12.9938
-42.2400
37.9900
ITP
113.7776
79.4059
1.6400
402.4900
ACP
55.6630
69.8964
1.1500
290.4800
APP
29.8396
34.4943
0.0000
172.8300
C2C
139.6012
142.8235
-44.4400
692.9700
SZ
6.8764
0.3284
6.2700
7.3500
LEV
0.6146
0.4180
0.1400
2.2900
Source: Generated by the researcher from the annual reports and accounts of the sampled companies using STATA (Version 12).
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Table 4.1 discloses that the return on assets of the five conglomerate companies over the ten year period to 2012 have an average of 4.24% ranged from a negative return of 42.24% to a maximum of 37.99%. This means that for every one Naira worth of net investment, the industry had at worst made a loss of N42.24 and had at best earned a maximum of N37.99 kobo. Every firm in the industry could earn an average of 4.24% on its net investment with a high degree of risk, as returns varied at both sides of the scale by as large a margin as 12.99%. It took an average of 114 days to convert inventories into sales. While at a particular time, some firms in the industry were able to shorten this range to only 1 to 2 days others could not turn inventories into sales till after 403 days. The credit period the companies granted their clients averaged 56 days while they paid their creditors in 30 days on the average. Whereas their debtors could remain outstanding for a maximum of 291 days, the firms were not paying their bills earlier than single days. On the whole, the average C2C cycle was 140 days. This means that, the firms run their operations for an average of 140 days using suppliers‟ funds. 4.3
Correlation between the Variables of the Study
In an effort to establish the nature of the correlation between the dependent and the independent variables and also to ascertain whether or not multi-collinearity exists as a result of the correlation among variables, Correlation analysis assesses the inter-relationship and association between variables. The Pearson correlation analysis is used here to assess the relationship between the variables of Working Capital Management and Profitability, Table 4.2 is computed for this purpose. The correlation matrix in Table 4 provides some insights into which of the independent variables are related to the dependent variable ROA.
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Table 4.2: Correlation Coefficients of the Variables Variables
ROA
ITP
ACP
APP
C2C
SZ
ROA
1.0000
ITP
-0.0662
1.0000
ACP
0.1026
0.7448
1.0000
APP
0.0375
-0.0737
0.1356
1.0000
C2C
0.0044
0.9383
0.8708
-0.2161
1.0000
SZ
0.0102
-0.5628
-0.3813
0.0391
-0.5090
1.0000
-0.7323
-0.0470
-0.2349
-0.1710
-0.0998
0.1253
LEV
LEV
VIF
2.100 1.630 3.970 6.800 1.500 1.0000
1.140
Source: Generated by the researcher from the annual reports and accounts of the sampled companies using STATA (Version 12). From the above Table 4.2, the values on the diagonal are all 1.000, indicating that each variable is perfectly correlated with itself. The highest correlations with ROA is for LEV (0.7323) which is negative, which implies there is lack of multi-collinearity with ROA and all variables. Likewise, the correlations within the explanatory variables prove lack of multicollinearity as the highest correlation coefficient is that of ITP and C2C with a positive value of 0.9383. With regard to the nature of the correlation between the dependent and the independent variables, the relationship between ROA and ITP shows a negative and insignificant amounted to only -0.0662 which is less than 7%, which implies as ITP reduces by less than 6.62% ROA will increase by the same percentage. However, the correlation between ACP and Profitability is positive with about 0.1026 coefficients, which implies that as collection period of debtors increased the Profitability of conglomerate companies in Nigeria may increase by 10.26%. Similarly, the nature of the correlation between ROA and APP show a positive and insignificant amounted to only 0.0375 which is less than 4%, which implies as APP increase by less than 4% ROA will increase by the same percentage. Likewise, C2C and ROA correlation is positive but with not too significant value of 0.0044 with implies an increase of C2C will slightly increase the Profitability of conglomerate companies in Nigeria. 61
Similarly from the Table 4.2, The VIF which is simply the reciprocal of TV ranges from 1.14 to 6.80, and this indicates the absence of Multi-collinearity. VIF shows multi-collinearity when its value exceeds 10 (Tobachnick & Fidell, 1996; as cited in Sabari, 2012). 4.4
Impact of Inventory Turnover Period on Profitability
As mentioned earlier, one of the challenges for a working capital manager is to have all the companies‟ managers to agree about how to manage the inventory. Each manager has his own interests he first and foremost would like to satisfy which complicates the task of reaching a joint decision. Each company should find the balance that they will benefit most from (Pass & Pike, 2007). In order to determine the impact of inventory policy in the management of working capital on the Profitability of conglomerate companies in Nigeria, the first regression equation, ROAit = α0 + α1 ITPit + α2 SZit + α3 LEVit + ϵit, in our model is run, using OLS, Fixed Effect regression and Random-effects GLS regression in which a Hausman specification test is also run to checks a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results, with the dependent variable ROA and the independent variable ITP, and control variables SIZE and LEV. The regression results of the Impact of Inventory Turnover Period on Profitability is evaluated from the model summary as presented in Table 4.3
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Table 4.3: Regression Results of the Impact of Inventory Turnover Period on Profitability
Variables
Coef.
OLS Std. Err.
T
P
Coef.
FIXED-EFFECT Std. Err. T
p
Coef.
RANDOM-EFFECT Std. Err. Z
p>|z|
ITP
-0.0103
0.0196
-0.5200
0.6020
-0.0671
0.0355
-1.8900
0.0650
-0.0103
0.0196
-0.5200
0.6000
SZ
2.6901
4.7721
0.5600
0.5760
3.4738
6.9683
0.5000
0.6210
2.6901
4.7721
0.5600
0.5730
LEV
-23.1184
3.1015
-7.4500
0.0000
-25.5886
5.9486
-4.3000
0.0000
-23.1184
3.1015
-7.4500
0.0000
Cons
1.1152
33.9714
0.0300
0.9740
3.7059
49.6407
0.0700
0.9410
1.1152
33.9714
0.0300
0.9740
R-squared
0.5495
Adj R-squared
0.5202
Within
0.3784
0.3414
Between
0.7119
0.9491
Overall
0.4715
0.5495
0.0002
0.0000
F value
18.71
Prob>F Hausman test (Prob>Chi)
0.0000
0.2117
Source: Generated by the researcher from the annual reports and accounts of the sampled companies using STATA (Version 12).
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In appraising the Model 1, based on the regression result in Table 4.3, the results of OLS show the coefficient of determinations “R-square” shows 52.02% indicating that the variables considered in the model accounts for about 52.02% change in the dependent variables, that is ROA, while the remaining of the change is as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. From the same OLS result in Table 4.3 it can be seen clearly that t-value of ITP -0.5200 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the tvalue is lower than 1.96 that means the ITP has no significant influence on the dependent variable as the higher the t-value the higher the relevance of the variable. Likewise, using Two-tall test p-value of ITP 0.6020 is higher than 0.05, and for a null hypothesis to be rejected the p-value has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the ITP has no significant influence on the dependent variable (ROA) as the p-value of 0.600 is higher than 0.05. Also considering the Hausman specification test result the Random-effects GLS regression showed that The coefficient of determinations “R-square” shows the within and between values of 34.14% and 94.91% which are highly impressive, while the overall R2 is 54.95%, indicating that the variables considered in the model account for about 55% change in the dependent variables, that is Profitability, while about 45% change may be as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. Similarly from Random-effect model result of the Table 4.3 it can be seen clearly that z-value of -0.52 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the z-value is lower than 1.96 that means the ITP has no significant influence on the dependent variable as the higher the z-value the higher the relevance of the variable.
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Furthermore, using Two-tall test p-value, for a null hypothesis to be rejected the p-value has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the ITP has no significant influence on the dependent variable (ROA) as the pvalue of 0.600 is higher than 0.05. In general, the overall probability is positively significant at 5%, and of all the independent variables in this study; however, ITP has no significant influence on the dependent variable as its z-value of -0.5200 is lower than 1.96 and the p-value of 0.803 is higher than 0.05. This is in line with Narware (2004) and Gill, Biger & Mathur (2010), whose found a statistically insignificant association between Profitability and inventory turnover and support the findings of Alipour (2011); Deloof (2003); Lee, Song, & Lee (2009); Panigrahi (2013); and Usama (2012) are postulating that there is a inverse relationship between the inventory turnover and Profitability. However, oppose with Ali (2011); Gill, Biger & Mathur (2010); Padachi (2006); Rimo & Panbunyuen (2010); Soekhoe (2012); and Warnes (2013) that attest a positive relationship. Thus, the model equation can be written as: Profitability (ROA) it = 1.115151it –α1 0.0102807it+ α2 2.690063it – α3 23.11843it + ϵit In the overall considering both correlation and regression results, the result of correlation between ROA and ITP show a negative 0.0662 which implies as ITP reduces by less than 7% ROA will increase by the same percentage and on the other hand both z-value and p-value reveal an insignificant relationship between ITP and ROA, thus it will be concluded that relationship between the Inventory turnover management and Profitability of listed conglomerate companies in Nigeria is a negative and insignificant.
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4.5
Impact of Average Collection Period on Profitability
Accounts receivable management, which is also known as debtor management, is a company giving their customers a specific credit term to pay for products or services. These credit terms, which are called trade credit, can help ease customer‟s financial frictions. Customers who buy products or service on trade credit are called sundry debtor for the company. Account receivable is a major component in business finance. In order to determine the impact of account collection period in the management of working capital on the Profitability of conglomerate companies in Nigeria, the second regression equation, ROAit = α0 + α1 ACPit + α2 SZit+ α3 LEVit+ ϵit, in our model is run, using OLS, Fixed Effect regression and Random-effects GLS regression in which a Hausman specification test is also run to check a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results, with the dependent variable ROA and the independent variable ACP, and control variables SIZE and LEV. The regression result of the Impact of Account Receivable Period on Profitability is evaluated from the model summary as presented in Table 4.4
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Table 4.4: Regression Results of the Impact of Account Receivable Period on Profitability
Variables ACP
Coef.
OLS Std. Err.
T
P
Coef.
FIXED-EFFECT Std. Err. T
P>|t|
Coef.
RANDOM-EFFECT Std. Err. Z
P>|z|
-0.0075
0.0204
-0.3700
0.7150
-0.0450
0.0309
-1.4500
0.1530
-0.0075
0.0204
-0.3700
0.7130
3.5271
4.2459
0.8300
0.4100
4.4866
7.1256
0.6300
0.5320
3.5271
4.2459
0.8300
0.4060
LEV
-23.4031
3.1717
-7.3800
0.0000
-26.1050
6.1441
-4.2500
0.0000
-23.4031
3.1717
-7.3800
0.0000
Cons
-5.2184
29.6595
-0.1800
0.8610
-8.0702
50.3694
-0.1600
0.8730
-5.2184
29.6595
-0.1800
0.8600
SZ
R-squared
0.5482
Adj R-squared
0.5187
Within
0.3578
0.3358
Between
0.8305
0.9524
Overall
0.5156
0.5482
0.0003
0.0000
F value
18.60
Prob>F Hausman test (Prob>Chi)
0.0000
0.3849
Source: Generated by the researcher from the annual reports and accounts of the sampled companies using STATA (Version 12).
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In evaluating the Model 2, based on the regression result in Table 4.4, the results of OLS show the coefficient of determinations “R-square” shows 54.82% indicating that the variables considered in the model accounts for about 54.82% change in the dependent variables, that is ROA, while the remaining of the change is as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. From the same OLS result in Table 4.4 it can be seen clearly that t-value of ACP -0.3700 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the tvalue is lower than 1.96 that means the ACP has no significant influence on the dependent variable as the higher the t-value the higher the relevance of the variable. Likewise, using Two-tall test p-value of ACP 0.7150 is higher than 0.05, and for a null hypothesis to be rejected the p-value has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the ACP has no significant influence on the dependent variable (ROA) as the p-value of 0.7150 is higher than 0.05. Moreover, considering the Hausman specification test result the Random-effects GLS regression showed that the coefficient of determinations “R-square” shows the within and between values of 33.58% and 95.24% which are also impressive, while the overall R2 is 54.82%, indicating that the variables considered in the model account for about 55% change in the dependent variables, that is Profitability, while about 45% change may be as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. In addition, from Random-effect model 2 result of the Table 4.4 it can be seen clearly that zvalue of -0.37 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the z-value is lower than 1.96 that means the ACP has no significant influence on the dependent variable (ROA) as the higher the z-value the higher the relevance of the
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variable. Moreover, using Two-tall test p-value, for a null hypothesis to be rejected the pvalue has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the ACP has no significant influence on the dependent variable (ROA) as the p-value of 0.713 is higher than 0.05. Predominantly, the overall probability is positively significant at 5%, and of all the independent variables in this study; however, ACP individually has no significant influence on the dependent variable as its z-value of -0.37 is lower than 1.96 and the p-value of 0.713 is higher than 0.05. This is in line with Ganesen (2007), Lee, Song, & Lee (2009); Ogundipe, Idowu, & Ogundipe, (2012); Ramachandran & Janakiraman (2009); and Sharma & Kumar (2011) who found an insignificant positive association between Profitability and average collection period. However, contradicted the findings Padachi (2006); Raheman & Nasr (2007); Mathuva (2009); Ahmadi, Arasi & Garajafary (2012); Raheman & Nasr (2007); Raheman, Afza, Qayyum & Bodla (2010); Ray (2012); Soekhoe (2012); Suhail & Lahcen (2011); and Usama (2012) whose discovered significant negative relationship between accounts receivable and Profitability. Consequently, the Model 2 equation can be written as: Profitability (ROA)it = -5.218443it – α1 0.0074866it+ α2 3.527067it – α3 23.40312it + ϵit In general taking into consideration of both correlation and regression outcomes, the result of correlation between ROA and ACP prove positive with about 0.1026 coefficients, which implies that as collection period of debtors increased the Profitability of conglomerate companies in Nigeria may increase by 10.261%, conversely both z-value and p-value reveal an insignificant relationship between ACP and ROA, for this reason it will be deduced that relationship between the Receivable conversion period and Profitability of listed conglomerate companies in Nigeria is a positive, however, insignificant.
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4.6
Impact of Average Payment Period on Profitability
Accounts payable or trade creditors as sometimes called form a major part of the outside debt of small businesses which have less access to long-term funds. According to Bates, Kahle, & Stulz (2009), trade creditors form a heterogeneous group, and sums other than pure trade credit and debt are often included. In order to establish the impact of account Payable period in the management of working capital on the Profitability of conglomerate companies in Nigeria, the third regression equation, ROAit = α0 + α1 APPit + α2 SZit+ α3 LEVit+ ϵit, in our model is run, using OLS, Fixed Effect regression and Random-effects GLS regression in which a Hausman specification test is also run to check a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results, with the dependent variable ROA and the independent variable APP, and control variables SIZE and LEV. The regression result of the Impact of Account Payable Period on Profitability is evaluated from the model summary as presented in Table 4.5.
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Table 4.5: Regression Result of the Impact of Account Payable Period on Profitability
OLS Variables
Coef.
Std. Err.
APP
-0.0366
0.0376
SZ
4.3332
3.9263
LEV
-23.7051
3.1277
Cons
-9.9007
26.8068
R-squared
0.5560
Adj R-squared
0.5270
T 0.9700 1.1000 7.5800 0.3700
P
Coef.
0.3360
-0.0438
0.2750
4.7980 23.3520 13.1008
0.0000 0.7140
FIXED-EFFECT Std. Err. T 0.0417 1.0500 7.2970 6.1175 51.6195
0.6600 3.8200 0.2500
0.3000
RANDOM-EFFECT Std. Coef. Err. Z -0.0369 0.0377 0.9800
0.3270
0.5140
4.3375
3.9852
0.2760
0.0000
-23.7073
3.1748
0.8010
-9.9193
27.2275
P
1.0900 7.4700 0.3600
Within
0.3427
0.3422
Between
0.9574
0.9597
Overall
0.5554
0.5560
0.0005
0.0000
F value
19.20
Prob>F Hausman test (Prob>Chi)
0.0000
P>|z|
0.0000 0.7160
0.9805
Source: Generated by the researcher from the annual reports and accounts of the sampled companies using STATA (Version 12).
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In reviewing the Model 3, based on the regression result in Table 4.5, the results of OLS show the coefficient of determinations “R-square” shows 55.60% indicating that the variables considered in the model accounts for about 55.60% change in the dependent variables, that is ROA, while the remaining of the change is as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. So also considering the Hausman specification test result the Random-effects GLS regression showed that the coefficient of determinations “R-square” shows the within and between values of 34.22% and 95.97% which are also remarkable, while the overall R2 is 55.60%, indicating that the variables considered in the model account for about 56% change in the dependent variables, that is Profitability, while about 44% change may be as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. From the same OLS result in Table 4.5 it can be seen clearly that t-value of APP -0.9700 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the tvalue is lower than 1.96, that means the APP has no significant influence on the dependent variable as the higher the t-value the higher the relevance of the variable. Similarly, using Two-tall test p-value of APP 0.3360 is higher than 0.05, and for a null hypothesis to be rejected the p-value has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the APP has no significant influence on the dependent variable (ROA) as the p-value of 0.3360 is higher than 0.05. Additionally, from Random-effect model result of the Table 4.5 one can clearly observe that z-value of -0.98 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the z-value is lower than 1.96 that means the APP has no significant influence on the dependent variable (ROA) as the higher the z-value the higher the relevance of the
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variable. Similarly, using Two-tall test p-value, for a null hypothesis to be rejected the pvalue has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the APP has no significant influence on the dependent variable (ROA) as the p-value of 0.327 is higher than 0.05. In general, the overall probability is positively significant at 5%, however, APP independently has no significant influence on the dependent variable (ROA) as its z-value is lower than 1.96 and the p-value of 0.327 is higher than 0.05. This is in line with Gill, Biger & Mathur (2010) as well as Raheman, Afza, Qayyum & Bodla (2010), which found a statistically insignificant positive relationship between Profitability and average payment period. However, the finding disagree with Shah and Sana (2006), Nobanee & Al-Hajjar (2009) as well as Dong & Su (2010) that reported statistically significant positive relationship between average payment period and Profitability. However, contradict Deloof (2003); Lazaridis & Tryfonidis (2006); Lee, Song, & Lee (2009); Nobanee & AlHajjar (2009); and Raheman & Nasr (2007) that found accounts payable days is inversely related with firm Profitability. Hence, the model equation can be written as: Profitability (ROA)it = -9.919264it – α1 0.0369205it + α2 4.337479it – α3 23.70726it + ϵit On the whole, in view of mutual outcomes of correlation and random-effect GLS regression, the result of correlation between ROA and APP prove a positive result of 0.0375 which implies as APP increase the Profitability of conglomerate companies in Nigeria may increased, on the other hand both z-value and p-value reveal an insignificant relationship between APP and ROA, for this reason it will be deduced that relationship between the Payables deferral period and Profitability of listed conglomerate companies in Nigeria is a positive however insignificant.
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4.7
Impact of Cash-to-Cash Cycle on Profitability
C2C Cycle is “the length of time a company‟s cash is tied up in working capital before that money is finally returned when customers pay for the products sold or services rendered” (Churchill & Mullins, 2001). C2C Cycle is a unique financial performance metric that indicates how a firm is managing their capital across the supply chain. In order to establish the impact of C2C Cycle in the management of working capital on the Profitability of conglomerate companies in Nigeria, the fourth regression equation, ROAit = α0 + α1 C2Cit + α2 SZit+ α3 LEVit+ ϵit, in our model is run, using OLS, Fixed Effect regression and Random-effects GLS regression in which a Hausman specification test is also run to check a more efficient model against a less efficient but consistent model to make sure that more efficient model also gives consistent results, with the dependent variable ROA and the independent variable C2C, and control variables SIZE and LEV. The regression result of the Impact of C2C Cycle on Profitability is evaluated from the model summary as presented in Table 4.6.
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Table 4.6: Regression Results of the Impact of C2C Cycle on Profitability
Variables C2C
Coef.
OLS Std. Err.
T
P
Coef.
FIXED-EFFECT Std. Err. T
P
Coef.
RANDOM-EFFECT Std. Err. Z
P>|z|
-0.0021
0.0105
-0.2000
0.8400
-0.0264
0.0191
-1.3900
0.1730
-0.0021
0.0105
-0.2000
0.8400
3.6305
4.5784
0.7900
0.4320
3.1920
7.0994
0.4500
0.6550
3.6305
4.5784
0.7900
0.4280
LEV
-23.1917
3.1109
-7.4600
0.0000
-26.3587
6.2162
-4.2400
0.0000
-23.1917
3.1109
-7.4600
0.0000
Cons
-6.1799
32.1675
-0.1900
0.8480
2.1751
50.5956
0.0400
0.9660
-6.1799
32.1675
-0.1900
0.8480
SZ
R-squared Adj Rsquared
0.5472 0.5177
Within
0.3549
0.3301
Between
0.8142
0.9595
Overall
0.5017
0.5572
0.0003
0.0000
F value
18.53
Prob>F Hausman test (Prob>Chi)
0.0000
0.44251
Source: Generated by the researcher from the annual reports and accounts of the sampled companies using STATA (Version 12).
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In evaluating the Model 4, based on the regression result in Table 4.6, the results of OLS show the coefficient of determinations “R-square” shows 0.5472 indicating that the variables considered in the model accounts for about 54.72% change in the dependent variables, that is ROA, while the remaining of the change is as a result of other variables not addressed by this model. Likewise the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. Similarly, taking into consideration the Hausman specification test result, the Random-effects GLS regression showed that the coefficient of determinations “R-square” shows the within and between values of 33.01% and 95.95% which are also notable, while the overall R2 is 54.72%, indicating that the variables considered in the model account for about 55% change in the dependent variables, that is Profitability, while about 45% change may be as a result of other variables not addressed by this model. Equally the P-Value of 0.0000 which is less than 0.05 proved the model to be fit. From the same OLS result in Table 4.6 it can be seen clearly that t-value of C2C -0.2000 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the tvalue is lower than 1.96 that means the C2C has no significant influence on the dependent variable as the higher the t-value the higher the relevance of the variable. Likewise, using Two-tall test p-value of C2C 0.8400 is higher than 0.05, and for a null hypothesis to be rejected the p-value has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the C2C has no significant influence on the dependent variable (ROA) as the p-value of 0.8400 is higher than 0.05. Additionally, from Random-effect model result of the Table 4.6 one can clearly observe that z-value of -0.20 is lower than 1.96 (for a 95% confidence level) the null hypothesis will not be rejected as the z-value is lower than 1.96 that means the C2C cycle has no significant influence on the dependent variable (ROA) as the higher the z-value the higher the relevance
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of the variable. Similarly, using Two-tall test p-value, for a null hypothesis to be rejected the p-value has to be lower than 0.05 (for a 95% confidence level) or an alpha of 0.10 (for a 90% confidence level), thus the C2C has no significant influence on the dependent variable (ROA) as the p-value of 0.840 is higher than 0.05. In general, the overall probability is positively significant at 5%, and of all the independent variables in this model; however, C2C independently has no significant influence on the dependent variable (ROA) as its z-value is lower than 1.96 and the p-value of 0.840 is higher than 0.05. This is in line with Zariyawati, Annuar, Taufiq & AbdulRahim (2009), Lyroudi & Lazaridis (2000); Onwumere, Ibe & Ugbam (2012); Soekhoe (2012); and Leeper & Chambers (2013) who found a positive relationship between the cash conversion cycle and Profitability. However, oppose Falope & Ajilore (2009), Ramachandran & Janakiraman (2009), and Dong & Su (2010) that established significant negative relationship between Profitability and the C2C cycle. Therefore, the model equation can be written as: Profitability (ROA)it = -6.179934it – α1 0. 0021254it + α2 3.630541it – α3 23.1917it + ϵit Generally by looking at the result of correlation as well as that of random-effect GLS regression, the correlation between ROA and C2C provide evidence of positive value of 0.0044 with implies an increase of C2C cycle will slightly increase the Profitability of conglomerate companies in Nigeria. On the other hand both z-value and p-value reveal an insignificant relationship between C2C and ROA, for this reason it will be deduced that relationship between the C2C cycle and Profitability of listed conglomerate companies in Nigeria is a positive however insignificant.
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CHAPTER FIVE SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 5.1
Summary
Effective financial management is vital for business survival and ultimate growth. As such, almost all the decisions on the part of the finance managers pertaining to this valuable resource have much bearing upon the performance, risk and the market value of the firms. The financial management decisions of companies are basically concerned with three major areas: capital structure, capital budgeting, and working capital management. Among these major areas, the Working Capital Management (WCM) is an area of great significance for every company as it virtually affects its overall Profitability and liquidity.
The Chapter one comprises of 6 sections, thus: background to the study, statement of the research problem, objectives of the study, hypotheses of the study, scope and the significance of the study. Under the background to the study, it is explained that Finance is considered the lifeblood of business. It is stated under the problem of the study that lack of or perhaps missing of empirical evidence on the effect of Working Capital Management on firm Profitability in case of the Nigerian Conglomerate sector (to the best of the researcher‟s knowledge), as well as lack of general agreement regarding the influence that Working Capital Management variables have on corporate Profitability provided the reason for this study. The study therefore, is an attempt to fill this gap and examined the relationship between Working Capital Management and firm Profitability for the Nigerian Conglomerate sector.
It is explained, under the scope of the study, that the study examines the relationship between Working Capital Management and the Profitability of Nigerian Conglomerate companies. It explore on the relationship between Inventories, Receivables, Payables conversion period as 78
well as C2C cycle as Working Capital Management components and the Return on Asset (ROA) as a Profitability Proxy. The time frame of the study is from the year 2003 to 2012. The Chapter two covers the concept of Working Capital Management, Cash Management, and Operating Cycle. It gave an Overview of C2C Metric, Inventory Management and Average Number of Days Inventory, Accounts Receivable Management and Average Number of Days Account Receivables, Account Payables Management and Average Number of Days Account Payables, and Corporate Profitability. It also reviewed the Empirical Studies on Working Capital Management and Profitability as well as the theoretical framework. The findings of the studies reviewed in chapter two reveal diverse outcome where most of the studies used multiple regressions analysis and the frequent proxy for Profitability is ROA, while for Working Capital Management components are: Inventory Days; Account Receivables Days; Account Payable Days and C2C cycle. Under the theoretical framework, it is explained that three theories were found relevant to this study; Monetary, financial, and cash cycle theories were the theories that best explain this research. These three theories when applied to this research view the relationship in terms of cash (liquid asset) and liquidity management (monetary theory) cash management and liquidity (financial theory) and among dynamic liquidity indicators; days of inventory, days receivable outstanding, days payable outstanding, C2C metric and cash management (cash cycle theory). In the Chapter three, Ex-post facto design was adopted in order to establish a causal relationship between Working Capital Management and Profitability of the listed Conglomerate Firms in Nigeria. The population of this study as stated comprises of the 6 Conglomerate Firms that were listed on the Nigerian Stock Exchange on or before 31 December, 2003. This criterion was established with a view to ensuring that the firms selected have published their financial statements for the period to be covered by this study in
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order to ensure the availability of relevant data. As a result of this filter, 5 firms emerged as the sample of this study. Documentary source of data was used. Accounting data were obtained primarily from the published annual reports and accounts of the firms. So also Chapter three stated Dependent and Explanatory Variables, the dependent variable in the study is the companies‟ Profitability which is in consistent with the works of Falope & Ajilore (2009) and Afza & Nazir (2009). The explanatory variables consist of independent and control variables. The independent variables of average collection and payment periods, and inventory turnover as measures of working capital management, were used in previous studies of Padachi (2006), Sha and Sana (2006), Falope & Ajilore (2009) and Gill, Biger & Mathur (2010), Hayajneh & Yassine (2011), with Raheman & Nasr (2007) and Zariyawati, Annuar, Taufiq & Abdul Rahim (2009) these are basically the key variables that influence working capital management. Along the same line, the present study in addition to the Working Capital Management variables, take into consideration two control variables relating to the companies. The measure of the logarithms of total sales of the companies is adopted for size as one of the control variables. This measure was used in Raheman & Nasr (2007), Dong & Su (2010) and Gill, Biger & Mathur (2010). Similarly, consistent with the works of Raheman & Nasr (2007), Zariyawati, Annuar, Taufiq & Abdul Rahim (2009), Dong & Su (2010) and Gill, Biger & Mathur (2010), leverage as measured by the ratio of total debt to total assets is added as the second control variable. Three techniques of data analysis were used to analyze the generated data. They are Descriptive Statistics, Pearson Correlation, and Multiple Regression alongside with Hausman Specification Test as the decision rules. Descriptive statistics was used to compute the summary statistics that describe the summary statistics for the variables of the study; Pearson
80
Correlation is used to establish the nature of the relationship between the dependent and the independent variables and also to ascertain whether or not multi-collinearity exists as a result of the correlation among variables; Fixed-Effect and Random-effects GLS regression is used to test the four (4) models in which a Hausman specification test is run to check a more efficient model against a less efficient but consistent model to make sure that more efficient model also gives consistent results. In the Chapter four, null hypotheses have been tested, and results of the processed data were presented and discussed. Firstly, the test results of the first null hypothesis of the study that Inventory Conversion Period does not have effect on the Profitability of the listed Conglomerate Firms in Nigeria, using Pearson correlation, fixed effect regression and random effect GLS regression analysis. The results show that Inventory conversion period has negative but insignificant relationship with the Profitability of the listed Conglomerate Firms in Nigeria. Therefore, the study concludes that Inventory Conversion Period does not have a significant impact on the Profitability of the listed Conglomerate Firms in Nigeria based on the documentary evidence. Secondly, the test results of the second null hypothesis of the study that Receivable Conversion Period does not have effect on the Profitability of the listed Conglomerate Firms in Nigeria is presented. This test used documentary data to measure the relationship between the two variables using Pearson correlation, fixed effect regression and random effect GLS regression analysis. The analyses of the results show that Receivable Conversion Period is positively related to Profitability of the listed Conglomerate Firms in Nigeria, however, the relationship is insignificant. Thirdly, the test results of the third null hypothesis of the study that Payables deferral period does not have effect on the Profitability of the listed Conglomerate Firms in Nigeria is
81
discussed. The analyses of the results show that Payables deferral period is positively related to Profitability of the listed Conglomerate Firms in Nigeria, however, the relationship is insignificant. Lastly, the test results of the fourth null hypothesis of the study that C2C cycle does not have effect on the Profitability of the listed Conglomerate Firms in Nigeria is analyzed. The analyses of the results show that C2C cycle is positively related to the efficiency of the listed Conglomerate Firms in Nigeria, though the relationship is statistically insignificant. 5.2
Conclusions
Working Capital Management is important part in firm financial management decision. The ability of the firm to continuously operate in longer period is dependent on how they deal with investment in working capital management. The optimal Working Capital Management could be achieved by firm that manage the trade off between Profitability and liquidity. Based on the research findings the following conclusions are drawn: i.
Inventory conversion period is negatively related to Profitability of the listed Conglomerate Firms in Nigeria, however, the relationship is insignificant based on the documentary evidence. When a company maintains a high level of inventory with generous trade credit policy, the sales are likely to increase, hence improving the Profitability of the company. However, the increases in inventory days mean the cost for storing the inventories will increase.
ii.
Receivable conversion period is positively related to Profitability of the listed Conglomerate Firms in Nigeria, however, the relationship is insignificant. Increases in the debtor‟s days have negative impact on company‟s performance as they will find it very difficult to generate cash to manage the other expenses.
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iii.
Payables deferral period is positively related to Profitability of the listed Conglomerate Firms in Nigeria however the relationship is insignificant.
iv.
C2C cycle is positively related to the efficiency of the listed Conglomerate Firms in Nigeria, though the relationship is statistically insignificant.
v.
In the overall it can be deduce that Working Capital Management affects Nigerian conglomerate companies‟ Profitability however, insignificantly.
5.3
Recommendations
Based on the conclusions of this study, the following recommendations are offered: i.
In the case of high cost/level inventories, there should be periodic stock taking so as to discover in time, the slow moving stocks (if any) to avoid over investment in such stocks. Further, if there are not any more demand for the products the stock needs to be written off which can increase the obsolescence stocks. Management should therefore, adopt a more sophisticated inventory management/control measure.
ii.
By implication, increases or decreases in average collection period affect the firm‟s profits in the same direction. Further debtors outstanding for long period may needs to be written off by the management. So speeding up in the debtor‟s collection is very vital for the companies under study. The collected cash can be invested in day to day operations to expand business operations and increase Profitability. They should also create a proper credit policy, which will attract customers, while keeping their risks low.
iii.
Furthermore, the manager should consider that, increases or decreases in average payment period affect the firm‟s profits in the same direction. Hence, there is need
83
for effective management of these components for any company that wants to increase the firm‟s value in terms of Profitability. They should also optimize the timing of payments, by placing the time of payment as late as possible, to maximize the advantages of (external) financing. iv.
Also, Managers should try as much as possible to maintain (manage) their cash operating cycle. This is because, (even as indicated by the C2C cycle) the longer the cash cycle the higher profitable the firm becomes; meaning that a longer operating cycle is more desirable and advisable as it affects Profitability.
v.
Similarly, companies should sufficiently plan and control their operations, amend the shortfalls as noted, consider the principles of finance in their decision making, utilize the services of professionals in complex business areas, and perform periodic stock taking.
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91
APPENDIX 1 LISTED NIGERIAN CONGLOMERATE COMPANIES DATA SHEET AG LEVENTIS DATA SHEET
YEAR
TURNOVER N'000
COST OF SALE N'000
PBT N'000
TRADE
STOCKS
DEBTORS
(FG&WIP)
N'000
N'000
FIXED ASSET N'000
2003
2,754,591.00
2,134,055.00
147,195.00
556,804.00
763,953.00
1,438,338.00
2004
2,805,769.00
2,134,745.00
261,437.00
108,988.00
561,078.00
1,451,835.00
2005
3,141,808.00
2,187,491.00
402,165.00
136,172.00
564,212.00
2,460,480.00
2006
3,251,829.00
2,198,604.00
459,543.00
10,272.00
410,708.00
2,404,270.00
2007
2,452,087.00
1,434,671.00
690,722.00
12,460.00
335,492.00
4,780,851.00
2008
2,858,944.00
1,667,472.00
841,447.00
37,344.00
589,070.00
7,286,104.00
2009
3,585,968.00
2,141,724.00
1,038,492.00
60,123.00
974,225.00
7,362,627.00
2010
5,475,461.00
3,709,681.00
1,081,358.00
242,645.00
1,692,079.00
7,876,303.00
2011
8,501,055.00
6,067,349.00
1,130,437.00
3,070,601.00
1,692,079.00
8,931,859.00
2012
7,515,354.00
5,189,628.00
1,598,041.00
3,529,494.00
1,862,582.00
8,666,592.00
92
AG LEVENTIS DATA SHEET
YEAR
CURRENT
TRADE
CURRENT
LONG
ASSET
CREDITORS
LIABILITIES
TERMDEBT
N'000
N'000
N'000
N'000
TOTAL TOTAL ASSET
LIABILITIES
N'000
N'000
2003
1,940,258.00
128,638.00
1,813,145.00
-
3,378,596.00
1,813,145.00
2004
2,050,421.00
120,296.00
1,217,127.00
-
3,502,256.00
1,217,127.00
2005
1,268,686.00
119,306.00
1,268,686.00
-
3,729,166.00
1,268,686.00
2006
1,222,240.00
101,074.00
1,442,848.00
-
3,626,510.00
1,442,848.00
2007
2,909,535.00
114,003.00
1,166,990.00
-
7,690,386.00
1,166,990.00
2008
2,825,870.00
106,280.00
1,929,877.00
-
10,111,974.00
1,929,877.00
2009
3,519,557.00
89,862.00
2,127,413.00
391,511.00
10,882,184.00
2,518,924.00
2010
5,140,159.00
1,042,819.00
2,790,113.00
1,487,455.00
13,016,462.00
4,277,568.00
2011
14,543,807.00
2,090,807.00
3,239,641.00
2,407,047.00
23,475,666.00
5,646,688.00
2012
15,626,502.00
2,074,212.00
3,815,466.00
2,127,492.00
24,293,094.00
5,942,958.00
AG LEVENTIS DATA SHEET
YEAR
ROA
ITP
ACP
APP
CCC
SZ
LEV
2003
4.36
130.66
73.78
15.97
188.47
6.44
0.54
2004
7.46
95.93
14.18
14.93
95.18
6.45
0.35
2005
10.78
94.14
15.82
14.45
95.51
6.50
0.34
2006
12.67
68.18
1.15
12.18
57.15
6.51
0.40
2007
8.98
85.35
1.85
21.06
66.15
6.39
0.15
2008
8.32
128.94
4.77
16.89
116.82
6.46
0.19
2009
9.54
166.03
6.12
11.12
161.03
6.55
0.23
2010
8.31
166.49
16.17
74.49
108.17
6.74
0.33
2011
4.82
101.79
131.84
91.32
142.31
6.93
0.24
2012
6.58
131.00
171.42
105.92
196.50
6.88
0.24
93
CHELLARAMS DATA SHEET TRADE YEAR
TURNOVER
COST OF SALE
PBT
DEBTORS
FIXED ASSET
N'000
N'000
N'000
N'000
N'000
2003
4,653,151.00
4,025,134.00
69,180.00
305,376.00
773,943.00
922,301.00
2004
6,089,803.00
5,200,910.00
121,029.00
439,791.00
1,570,836.00
1,254,916.00
2005
7,784,321.00
6,702,680.00
184,610.00
613,394.00
1,125,504.00
1,140,616.00
2006
8,357,089.00
7,127,215.00
192,833.00
838,741.00
1,383,178.00
1,690,050.00
2007
9,929,884.00
8,487,180.00
217,925.00
838,396.00
1,706,650.00
1,808,471.00
2008
12,571,939.00
10,723,292.00
243,978.00
879,909.00
2,313,560.00
2,391,415.00
2009
14,398,832.00
12,499,578.00
133,257.00
1,004,548.00
2,728,668.00
2,434,159.00
2010
17,824,410.00
15,455,159.00
372,039.00
777,497.00
2,931,189.00
2,794,376.00
2011
21,413,298.00
19,197,747.00
310,708.00
1,056,748.00
3,076,801.00
2,998,260.00
2012
22,554,330.00
20,030,679.00
252,300.00
1,080,674.00
5,750,161.00
3,210,988.00
94
N'000
STOCKS (FG&WIP)
CHELLARAMS DATA SHEET
YEAR
CURRENT
TRADE
CURRENT
LONG
ASSET
CREDITORS
LIABILITIES
TERMDEBT
N'000
N'000
N'000
TOTAL
N'000
TOTAL ASSET
LIABILITIES
N'000
N'000
2003
1,222,871.00
144,770.00
1,033,103.00
74,966.00
2,145,172.00
1,108,069.00
2004
2,213,366.00
192,465.00
1,953,310.00
77,777.00
3,468,282.00
2,031,087.00
2005
2,103,398.00
173,039.00
1,764,544.00
66,608.00
3,244,014.00
1,831,152.00
2006
2,864,430.00
140,211.00
2,433,480.00
113,424.00
4,554,480.00
2,546,904.00
2007
3,191,617.00
355,463.00
2,714,375.00
113.00
5,000,088.00
2,714,488.00
2008
3,999,389.00
528,788.00
3,719,406.00
90,601.00
6,390,804.00
3,810,007.00
2009
5,148,518.00
602,679.00
5,558,706.00
9,575.00
7,582,677.00
5,568,281.00
2010
5,392,889.00
1,261,852.00
5,469,914.00
446,353.00
8,187,265.00
5,916,267.00
2011
6,372,635.00
1,764,821.00
5,246,000.00
1,821,077.00
9,370,895.00
7,067,077.00
2012
9,363,976.00
1,368,804.00
8,414,311.00
1,966,242.00
12,574,964.00
10,380,553.00
CHELLARAMS DATA SHEET
YEAR
ROA
ITP
2003
3.22
70.18
2004
3.49
2005
ACP
APP
CCC
23.95
9.53
84.60
6.67
0.52
110.24
26.36
9.81
126.79
6.78
0.59
5.69
61.29
28.76
6.84
83.21
6.89
0.56
2006
4.23
70.84
36.63
5.21
102.25
6.92
0.56
2007
4.36
73.40
30.82
11.10
93.11
7.00
0.54
2008
3.82
78.75
25.55
13.07
91.23
7.10
0.60
2009
1.76
79.68
25.46
12.78
92.37
7.16
0.73
2010
4.54
69.23
15.92
21.64
63.51
7.25
0.72
2011
3.32
58.50
18.01
24.36
52.15
7.33
0.75
2012
2.01
104.78
17.49
18.11
104.16
7.35
0.83
95
SZ
LEV
JOHN HOLT DATA SHEET
YEAR
TURNOVER
COST OF SALE
PBT
N'000
N'000
N'000
2003
12,042,000.00
10,661,000.00
(118,000.00)
2004
16,360,000.00
14,105,000.00
2005
9,159,000.00
2006
TRADE
STOCKS
DEBTORS
(FG&WIP)
N'000
FIXED ASSET
N'000
N'000
468,000.00
2,228,000.00
1,368,000.00
237,000.00
2,117,000.00
2,612,000.00
1,517,000.00
7,546,000.00
(572,000.00)
1,059,000.00
2,443,000.00
1,059,000.00
11,914,000.00
9,947,000.00
(367,000.00)
1,233,000.00
2,858,000.00
1,233,000.00
2007
16,538,000.00
13,961,000.00
106,000.00
910,000.00
4,499,000.00
1,560,000.00
2008
20,866,000.00
18,008,000.00
(38,000.00)
1,333,000.00
4,987,000.00
1,926,000.00
2009
18,414,000.00
15,791,000.00
(13,000.00)
1,191,000.00
4,866,000.00
2,508,000.00
2010
10,228,000.00
8,446,000.00
10,000.00
820,000.00
3,860,000.00
2,647,000.00
2011
5,897,000.00
4,865,000.00
(1,933,000.00)
391,000.00
1,144,000.00
2,919,000.00
2012
2,728,000.00
2,083,000.00
(1,787,000.00)
226,000.00
786,000.00
3,041,000.00
96
JOHN HOLT DATA SHEET
YEAR
CURRENT
TRADE
CURRENT
LONG
ASSET
CREDITORS
LIABILITIES
TERMDEBT
N'000
N'000
N'000
TOTAL
N'000
TOTAL ASSET
LIABILITIES
N'000
N'000
2003
3,136,000.00
952,000.00
4,276,000.00
228,000.00
4,504,000.00
4,504,000.00
2004
4,756,000.00
1,180,000.00
5,789,000.00
-
6,273,000.00
5,789,000.00
2005
3,685,000.00
788,000.00
4,378,000.00
186,000.00
4,744,000.00
4,564,000.00
2006
4,506,000.00
950,000.00
5,699,000.00
211,000.00
5,739,000.00
5,910,000.00
2007
6,746,000.00
567,000.00
8,123,000.00
232,000.00
8,306,000.00
8,355,000.00
2008
8,269,000.00
901,000.00
9,944,000.00
330,000.00
10,195,000.00
10,274,000.00
2009
7,813,000.00
846,000.00
11,313,000.00
1,690,000.00
10,321,000.00
13,003,000.00
2010
6,494,000.00
373,000.00
9,873,000.00
1,953,000.00
9,141,000.00
11,826,000.00
2011
1,683,000.00
183,000.00
7,772,000.00
669,000.00
4,602,000.00
8,441,000.00
2012
1,190,000.00
243,000.00
9,079,000.00
597,000.00
4,231,000.00
9,676,000.00
JOHN HOLT DATA SHEET
YEAR
ROA
ITP
ACP
APP
CCC
SZ
LEV
2003
-2.62
76.28
14.19
23.66
66.80
7.08
1.00
2004
3.78
67.59
47.23
22.17
92.65
7.21
0.92
2005
-12.06
118.17
42.20
27.67
132.70
6.96
0.96
2006
-6.39
104.87
37.77
25.31
117.34
7.08
1.03
2007
1.28
117.62
20.08
10.76
126.94
7.22
1.01
2008
-0.37
101.08
23.32
13.26
111.14
7.32
1.01
2009
-0.13
112.47
23.61
14.20
121.89
7.27
1.26
2010
0.11
166.81
29.26
11.70
184.37
7.01
1.29
2011
-42.00
85.83
24.20
9.97
100.06
6.77
1.83
2012
-42.24
137.73
30.24
30.91
137.05
6.44
2.29
97
SCOA DATA SHEET COST OF YEAR
TURNOVER
SALE
N'000
N'000
2003
5,013,702.00
3,618,423.00
2004
4,492,589.00
2005
PBT N'000
TRADE
STOCKS
DEBTORS
(FG&WIP)
FIXED ASSET
N'000
N'000
N'000
65,048.00
3,990,071.00
3,990,071.00
231,439.00
3,590,329.00
(348,277.00)
2,941,292.00
2,941,292.00
260,592.00
3,720,732.00
3,430,670.00
(820,405.00)
871,791.00
1,604,904.00
221,492.00
2006
1,868,175.00
1,478,102.00
848,932.00
525,167.00
959,762.00
189,305.00
2007
2,745,747.00
2,240,163.00
1,070,762.00
1,834,640.00
1,013,440.00
200,951.00
2008
2,988,478.00
2,159,792.00
411,271.00
1,517,852.00
1,250,359.00
248,581.00
2009
3,365,618.00
2,357,456.00
806,568.00
542,867.00
1,604,637.00
158,319.00
2010
3,145,920.00
2,082,492.00
220,025.00
1,065,980.00
1,268,397.00
153,773.00
2011
3,530,404.00
2,651,236.00
223,221.00
1,939,187.00
1,603,473.00
306,410.00
2012
6,018,968.00
4,551,513.00
176,762.00
1,467,497.00
2,583,689.00
751,110.00
98
SCOA DATA SHEET
YEAR
CURRENT
TRADE
CURRENT
LONG
ASSET
CREDITORS
LIABILITIES
TERMDEBT
TOTAL ASSET
N'000
N'000
N'000
N'000
N'000
TOTAL LIABILITIES N'000
2003
6,648,457.00
-
988,525.00
-
6,879,896.00
988,525.00
2004
5,531,184.00
-
928,169.00
-
5,791,776.00
928,169.00
2005
3,868,586.00
158,160.00
3,964,178.00
64,899.00
4,090,078.00
4,029,077.00
2006
3,328,965.00
62,041.00
2,672,460.00
58,830.00
3,518,270.00
2,731,290.00
2007
2,617,278.00
-
1,141,695.00
71,000.00
2,818,229.00
1,212,695.00
2008
3,450,778.00
-
1,743,034.00
71,000.00
3,699,359.00
1,814,034.00
2009
3,989,934.00
52,723.00
1,572,256.00
68,294.00
4,148,253.00
1,640,550.00
2010
3,998,188.00
160,535.00
1,452,084.00
77,441.00
4,151,961.00
1,529,525.00
2011
5,274,752.00
1,729,080.00
2,673,920.00
141,896.00
5,581,162.00
2,815,816.00
2012
5,828,202.00
1,700,789.00
3,594,201.00
211,649.00
6,579,312.00
3,805,850.00
SCOA DATA SHEET
YEAR
ROA
ITP
ACP
2003
0.95
402.49
290.48
2004
-6.01
299.02
APP
CCC
SZ
LEV
0.00
692.97
6.70
0.14
238.97
0.00
537.98
6.65
0.16
2005
20.06
170.75
85.52
12.22
244.06
6.57
0.99
2006
24.13
237.00
102.61
11.12
328.49
6.27
0.78
2007
37.99
165.12
243.88
0.00
409.01
6.44
0.43
2008
11.12
211.31
185.38
0.00
396.69
6.48
0.49
2009
19.44
248.44
58.87
5.93
301.39
6.53
0.40
2010
5.30
222.31
123.68
20.43
325.56
6.50
0.37
2011
4.00
220.75
200.49
172.83
248.41
6.55
0.50
2012
2.69
207.19
88.99
99.02
197.16
6.78
0.58
99
UAC DATA SHEET
YEAR
TURNOVER
COST OF SALE
PBT
TRADE
STOCKS
DEBTORS
(FG&WIP)
N'000
N'000
N'000
N'000
2003
14,848,011.00
11,164,986.00
2,895,799.00
160,848.00
887,174.00
10,989,966.00
2004
17,374,954.00
13,362,357.00
2,200,473.00
237,646.00
989,456.00
11,182,423.00
2005
16,982,650.00
12,753,371.00
2,308,924.00
651,994.00
691,656.00
13,685,744.00
2006
17,507,587.00
13,352,507.00
2,460,541.00
1,009,144.00
834,690.00
13,124,510.00
2007
18,161,094.00
14,329,271.00
2,449,247.00
901,854.00
868,494.00
12,151,276.00
2008
20,241,728.00
15,645,441.00
3,462,374.00
1,267,999.00
1,329,247.00
14,095,899.00
2009
20,134,638.00
15,430,730.00
2,614,797.00
1,408,550.00
1,269,773.00
13,634,963.00
2010
19,326,151.00
15,712,011.00
1,920,423.00
1,261,557.00
1,244,847.00
13,964,133.00
2011
6,334,230.00
3,489,000.00
629,934.00
40,963.00
556,970.00
10,313,788.00
2012
7,152,270.00
4,679,000.00
710,548.00
968,569.00
20,970.00
7,308,749.00
100
N'000
FIXED ASSET N'000
UAC DATA SHEET
YEAR
CURRENT ASSET N'000
TRADE
CURRENT
LONG
CREDITORS
LIABILITIES
TERMDEBT
TOTAL TOTAL ASSET
LIABILITIES
N'000
N'000
N'000
N'000
N'000
2003
3,819,907.00
3,260,758.00
5,462,733.00
1,570,028.00
14,809,873.00
7,032,761.00
2004
6,583,384.00
3,812,488.00
5,101,482.00
1,910,982.00
17,765,807.00
7,012,464.00
2005
6,247,505.00
1,351,788.00
4,097,859.00
1,853,520.00
19,933,249.00
5,951,379.00
2006
6,585,126.00
1,121,440.00
4,180,429.00
1,466,649.00
19,709,636.00
5,647,078.00
2007
7,687,081.00
1,659,282.00
5,984,328.00
1,312,830.00
19,838,357.00
7,297,158.00
2008
6,923,185.00
1,299,267.00
6,580,255.00
2,130,605.00
21,019,084.00
8,710,860.00
2009
7,409,373.00
1,195,567.00
7,296,518.00
2,176,658.00
21,044,336.00
9,473,176.00
2010
6,920,558.00
1,694,739.00
6,735,007.00
1,987,542.00
20,884,691.00
8,722,549.00
2011
5,375,730.00
979,601.00
6,103,594.00
2,048,354.00
15,689,518.00
8,151,948.00
2012
9,971,470.00
1,597,545.00
3,428,617.00
1,077,205.00
17,280,219.00
4,505,822.00
UAC DATA SHEET
YEAR
ROA
ITP
ACP
APP
CCC
SZ
LEV
2003
19.55
29.00
3.95
77.39
-44.44
7.17
0.47
2004
12.39
27.03
4.99
75.61
-43.59
7.24
0.39
2005
11.58
19.80
14.01
28.09
5.72
7.23
0.30
2006
12.48
22.82
21.04
22.26
21.60
7.24
0.29
2007
12.35
22.12
18.13
30.69
9.56
7.26
0.37
2008
16.47
31.01
22.86
22.01
31.87
7.31
0.41
2009
12.43
30.04
25.53
20.53
35.04
7.30
0.45
2010
9.20
28.92
23.83
28.58
24.16
7.29
0.42
2011
4.01
58.27
2.36
74.40
-13.78
6.80
0.52
2012
4.11
1.64
49.43
90.48
-39.41
6.85
0.26
101
APPENDIX 2 STATA VERSION 12.0 GENERATED RESULTS ___ ____ ____ ____ ____ (R) /__ / ____/ / ____/ ___/ / /___/ / /___/ 12.0 Statistics/Data Analysis Special Edition
Copyright 1985-2011 StataCorp LP StataCorp 4905 Lakeway Drive College Station, Texas 77845 USA 800-STATA-PC http://www.stata.com 979-696-4600
[email protected] 979-696-4601 (fax)
Single-user Stata network perpetual license: Serial number: 99611999968 Licensed to: SUNUSI FEDERAL UNIVERSITY, DUTSE Notes: 1.
(/v# option or -set maxvar-) 5000 maximum variables
. *(10 variables, 50 observations pasted into data editor) . summarize roa itp acp app c2c sz lev Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------roa | 50 4.2348 12.9938 -42.24 37.99 itp | 50 113.7776 79.40585 1.64 402.49 acp | 50 55.663 69.89637 1.15 290.48 app | 50 29.8396 34.49433 0 172.83 c2c | 50 139.6012 142.8235 -44.44 692.97 -------------+-------------------------------------------------------sz | 50 6.8764 .3283524 6.27 7.35 lev | 50 .6146 .418048 .14 2.29 . correlate roa itp acp app ccc sz lev (obs=50) | roa itp acp app c2c sz lev -------------+--------------------------------------------------------------roa | 1.0000 itp | -0.0662 1.0000 acp | 0.1026 0.7448 1.0000 app | 0.0375 -0.0737 0.1356 1.0000 c2c | 0.0044 0.9383 0.8708 -0.2161 1.0000 sz | 0.0102 -0.5628 -0.3813 0.0391 -0.5090 1.0000 lev | -0.7323 -0.0470 -0.2349 -0.1710 -0.0998 0.1253 1.0000 . xtset coy year panel variable: time variable: delta:
coy (strongly balanced) year, 2003 to 2012 1 unit
. . xtreg roa itp sz lev, fe Fixed-effects (within) regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3784 between = 0.7119 overall = 0.4715
corr(u_i, Xb)
F(3,42) Prob > F
= -0.5534
= =
8.52 0.0002
------------------------------------------------------------------------------
102
roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------itp | -.067074 .03545 -1.89 0.065 -.138615 .0044671 sz | 3.473791 6.968295 0.50 0.621 -10.5888 17.53638 lev | -25.5886 5.948569 -4.30 0.000 -37.5933 -13.5839 _cons | 3.705896 49.64065 0.07 0.941 -96.47298 103.8848 -------------+---------------------------------------------------------------sigma_u | 6.0328256 sigma_e | 8.9473161 rho | .31253892 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(4, 42) = 1.14 Prob > F = 0.3518 . estimates store fixed . xtreg roa itp sz lev, re Random-effects GLS regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3414 between = 0.9491 overall = 0.5495
corr(u_i, X)
Wald chi2(3) Prob > chi2
= 0 (assumed)
= =
56.12 0.0000
-----------------------------------------------------------------------------roa | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------itp | -.0102807 .0195992 -0.52 0.600 -.0486944 .0281331 sz | 2.690063 4.772061 0.56 0.573 -6.663005 12.04313 lev | -23.11843 3.101524 -7.45 0.000 -29.19731 -17.03956 _cons | 1.115151 33.97136 0.03 0.974 -65.46749 67.69779 -------------+---------------------------------------------------------------sigma_u | 0 sigma_e | 8.9473161 rho | 0 (fraction of variance due to u_i) -----------------------------------------------------------------------------. estimates store random . hausman fixed random ---- Coefficients ---| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------itp | -.067074 -.0102807 -.0567933 .0295394 sz | 3.473791 2.690063 .7837284 5.07785 lev | -25.5886 -23.11843 -2.470172 5.076025 -----------------------------------------------------------------------------b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:
Ho:
difference in coefficients not systematic chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 4.51 Prob>chi2 = 0.2117
. xtreg roa acp sz lev, fe Fixed-effects (within) regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg =
10 10.0
within = 0.3578 between = 0.8305
103
overall = 0.5156 corr(u_i, Xb)
max =
10
= =
7.80 0.0003
F(3,42) Prob > F
= -0.3838
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------acp | -.044955 .0308987 -1.45 0.153 -.1073111 .0174011 sz | 4.486572 7.125595 0.63 0.532 -9.893461 18.8666 lev | -26.105 6.144142 -4.25 0.000 -38.50438 -13.70562 _cons | -8.070196 50.36942 -0.16 0.873 -109.7198 93.57941 -------------+---------------------------------------------------------------sigma_u | 3.9561895 sigma_e | 9.0944768 rho | .15912246 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(4, 42) = 0.80 Prob > F = 0.5329 . estimates store fixed . xtreg roa acp sz lev, re Random-effects GLS regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3358 between = 0.9524 overall = 0.5482
corr(u_i, X)
Wald chi2(3) Prob > chi2
= 0 (assumed)
= =
55.81 0.0000
-----------------------------------------------------------------------------roa | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------acp | -.0074866 .0203587 -0.37 0.713 -.0473888 .0324157 sz | 3.527067 4.245931 0.83 0.406 -4.794804 11.84894 lev | -23.40312 3.171668 -7.38 0.000 -29.61947 -17.18676 _cons | -5.218443 29.65949 -0.18 0.860 -63.34998 52.9131 -------------+---------------------------------------------------------------sigma_u | 0 sigma_e | 9.0944768 rho | 0 (fraction of variance due to u_i) -----------------------------------------------------------------------------. estimates store random . hausman fixed random ---- Coefficients ---| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------acp | -.044955 -.0074866 -.0374684 .0232433 sz | 4.486572 3.527067 .9595046 5.722427 lev | -26.105 -23.40312 -2.701885 5.262224 -----------------------------------------------------------------------------b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:
Ho:
difference in coefficients not systematic chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 3.04 Prob>chi2 = 0.3849
. xtreg roa app sz lev, fe
104
Fixed-effects (within) regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3427 between = 0.9574 overall = 0.5554
corr(u_i, Xb)
F(3,42) Prob > F
= 0.0913
= =
7.30 0.0005
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------app | -.0437545 .0416654 -1.05 0.300 -.1278387 .0403296 sz | 4.798049 7.297014 0.66 0.514 -9.927921 19.52402 lev | -23.35197 6.11749 -3.82 0.000 -35.69756 -11.00637 _cons | -13.10076 51.61946 -0.25 0.801 -117.2731 91.07152 -------------+---------------------------------------------------------------sigma_u | 1.7596553 sigma_e | 9.2008277 rho | .03528581 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(4, 42) = 0.35 Prob > F = 0.8437 . estimates store fixed . xtreg roa app sz lev, re Random-effects GLS regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3422 between = 0.9597 overall = 0.5560
corr(u_i, X)
Wald chi2(3) Prob > chi2
= 0 (assumed)
= =
55.88 0.0000
-----------------------------------------------------------------------------roa | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------app | -.0369205 .0376894 -0.98 0.327 -.1107903 .0369494 sz | 4.337479 3.985244 1.09 0.276 -3.473457 12.14841 lev | -23.70726 3.17478 -7.47 0.000 -29.92971 -17.48481 _cons | -9.919264 27.22752 -0.36 0.716 -63.28422 43.44569 -------------+---------------------------------------------------------------sigma_u | .6857166 sigma_e | 9.2008277 rho | .0055237 (fraction of variance due to u_i) -----------------------------------------------------------------------------. estimates store random . hausman fixed random ---- Coefficients ---| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------app | -.0437545 -.0369205 -.0068341 .0177627 sz | 4.798049 4.337479 .4605699 6.112629 lev | -23.35197 -23.70726 .3552924 5.229193 -----------------------------------------------------------------------------b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:
Ho:
difference in coefficients not systematic
105
chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.18 Prob>chi2 = 0.9805 . xtreg roa c2c sz lev, fe Fixed-effects (within) regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3549 between = 0.8142 overall = 0.5017
corr(u_i, Xb)
F(3,42) Prob > F
= -0.5187
= =
7.70 0.0003
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------c2c | -.0264309 .0190785 -1.39 0.173 -.0649329 .012071 sz | 3.192008 7.099405 0.45 0.655 -11.13517 17.51919 lev | -26.35873 6.216185 -4.24 0.000 -38.9035 -13.81396 _cons | 2.175143 50.59558 0.04 0.966 -99.93086 104.2811 -------------+---------------------------------------------------------------sigma_u | 4.6532937 sigma_e | 9.1149032 rho | .20674297 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(4, 42) = 0.77 Prob > F = 0.5501 . estimates store fixed . xtreg roa c2c sz lev, re Random-effects GLS regression Group variable: coy
Number of obs Number of groups
= =
50 5
R-sq:
Obs per group: min = avg = max =
10 10.0 10
within = 0.3301 between = 0.9595 overall = 0.5472
corr(u_i, X)
Wald chi2(3) Prob > chi2
= 0 (assumed)
= =
55.60 0.0000
-----------------------------------------------------------------------------roa | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------c2c | -.0021254 .0104951 -0.20 0.840 -.0226954 .0184447 sz | 3.630541 4.578358 0.79 0.428 -5.342875 12.60396 lev | -23.1917 3.110884 -7.46 0.000 -29.28892 -17.09448 _cons | -6.179934 32.16751 -0.19 0.848 -69.2271 56.86723 -------------+---------------------------------------------------------------sigma_u | 0 sigma_e | 9.1149032 rho | 0 (fraction of variance due to u_i) -----------------------------------------------------------------------------. estimates store random . hausman fixed random ---- Coefficients ---| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------c2c | -.0264309 -.0021254 -.0243056 .0159324 sz | 3.192008 3.630541 -.4385332 5.425882
106
lev | -26.35873 -23.1917 -3.167032 5.381762 -----------------------------------------------------------------------------b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:
Ho:
difference in coefficients not systematic chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 2.79 Prob>chi2 = 0.4251
. . regress roa itp sz lev Source | SS df MS -------------+-----------------------------Model | 4546.36952 3 1515.45651 Residual | 3726.73272 46 81.0159288 -------------+-----------------------------Total | 8273.10225 49 168.838821
Number of obs F( 3, 46) Prob > F R-squared Adj R-squared Root MSE
= = = = = =
50 18.71 0.0000 0.5495 0.5202 9.0009
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------itp | -.0102807 .0195992 -0.52 0.602 -.0497318 .0291705 sz | 2.690063 4.772061 0.56 0.576 -6.915598 12.29572 lev | -23.11843 3.101524 -7.45 0.000 -29.36147 -16.87539 _cons | 1.115151 33.97136 0.03 0.974 -67.26565 69.49595 -----------------------------------------------------------------------------. regress roa acp sz lev Source | SS df MS -------------+-----------------------------Model | 4535.06696 3 1511.68899 Residual | 3738.03529 46 81.2616368 -------------+-----------------------------Total | 8273.10225 49 168.838821
Number of obs F( 3, 46) Prob > F R-squared Adj R-squared Root MSE
= = = = = =
50 18.60 0.0000 0.5482 0.5187 9.0145
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------acp | -.0074866 .0203587 -0.37 0.715 -.0484664 .0334933 sz | 3.527067 4.245931 0.83 0.410 -5.019548 12.07368 lev | -23.40312 3.171668 -7.38 0.000 -29.78735 -17.01888 _cons | -5.218443 29.65949 -0.18 0.861 -64.91991 54.48302 -----------------------------------------------------------------------------. regress roa app sz lev Source | SS df MS -------------+-----------------------------Model | 4599.65322 3 1533.21774 Residual | 3673.44903 46 79.8575877 -------------+-----------------------------Total | 8273.10225 49 168.838821
Number of obs F( 3, 46) Prob > F R-squared Adj R-squared Root MSE
= = = = = =
50 19.20 0.0000 0.5560 0.5270 8.9363
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------app | -.0366121 .0376352 -0.97 0.336 -.1123678 .0391435 sz | 4.333247 3.926343 1.10 0.275 -3.570071 12.23656 lev | -23.70514 3.127654 -7.58 0.000 -30.00078 -17.4095 _cons | -9.900671 26.80681 -0.37 0.714 -63.85999 44.05865 -----------------------------------------------------------------------------. regress roa c2c sz lev
107
Source | SS df MS -------------+-----------------------------Model | 4527.41752 3 1509.13917 Residual | 3745.68473 46 81.4279289 -------------+-----------------------------Total | 8273.10225 49 168.838821
Number of obs F( 3, 46) Prob > F R-squared Adj R-squared Root MSE
= = = = = =
50 18.53 0.0000 0.5472 0.5177 9.0237
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------c2c | -.0021254 .0104951 -0.20 0.840 -.0232509 .0190002 sz | 3.630541 4.578358 0.79 0.432 -5.585215 12.8463 lev | -23.1917 3.110884 -7.46 0.000 -29.45358 -16.92981 _cons | -6.179934 32.16751 -0.19 0.848 -70.92978 58.56991 -----------------------------------------------------------------------------. estat vif Variable | VIF 1/VIF -------------+---------------------sz | 1.36 0.735322 c2c | 1.35 0.739611 lev | 1.02 0.982556 -------------+---------------------Mean VIF | 1.24 . regress roa itp acp app c2c sz lev Source | SS df MS -------------+-----------------------------Model | 4924.20464 6 820.700774 Residual | 3348.8976 43 77.8813396 -------------+-----------------------------Total | 8273.10225 49 168.838821
Number of obs F( 6, 43) Prob > F R-squared Adj R-squared Root MSE
= = = = = =
50 10.54 0.0000 0.5952 0.5387 8.825
-----------------------------------------------------------------------------roa | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------itp | -445.2126 230.1624 -1.93 0.060 -909.3793 18.95416 acp | -445.1843 230.1607 -1.93 0.060 -909.3476 18.979 app | 445.1599 230.1649 1.93 0.060 -19.01186 909.3317 c2c | 445.1931 230.1611 1.93 0.060 -18.97105 909.3573 sz | 2.521018 4.708222 0.54 0.595 -6.974018 12.01605 lev | -22.94027 3.224411 -7.11 0.000 -29.44292 -16.43763 _cons | 3.624328 33.55964 0.11 0.915 -64.05514 71.3038 -----------------------------------------------------------------------------. estat vif Variable | VIF 1/VIF -------------+---------------------c2c | 6.80e+08 0.000000 itp | 2.10e+08 0.000000 acp | 1.63e+08 0.000000 app | 3.97e+07 0.000000 sz | 1.50 0.665033 lev | 1.14 0.874751 -------------+---------------------Mean VIF | 1.82e+08
108
