Dedicated to MY
Beloved Parents
&
Respected Teachers who are the Nation Builders
1
TABLE OF CONTENTS
List of Tables ……………………………………………………
Acknowledgement ………………………………………………
Chapter 1
ii
iv
INTRODUCTION
1.1
The Nature of Working Capital ……………………..……
5
1.2
Definition and Concept of Working Capital ……………..
5
1.3
Working Capital Management …………………………...
7
1.4
Working Capital Management and Researchers’ View ….
9
1.5
Significance of the Management of Working Capital ……
12
1.6
Research Objectives …………………………………...…
13
Chapter 2
LITERATURE REVIEW & HYPOTHESES
Chapter 3
RESEARCH METHODOLOGY
15
3.1
Variables of the Study ……………………………………
35
3.2
Control Variables ……………………………………….
37
3.3
Statistical Analysis ……………………………………….
37
3.4
Sample & Data ………………………………………….
40
Chapter 4
ANALYSIS
4.1
Descriptive Analysis ……………………………………..
42
4.2
Analysis of Variance (ANOVA) …………………………
48
4.3
Rank Order Correlation …………………………………..
55
4.4
Regression Analysis ……………………………………..
57
Chapter 5
CONCLUSION
76
REFERNCES
79
APPENDICES
86
2
List of Tables
TABLE 4.1
Descriptive Statistics for Study Variables
TABLE 4.2 A Results of ANOVA (F-test) and Test of Least Significant
Differences (LSD) for Total Current Assets / Total Assets
(TCA / TA)
B Results of ANOVA (F-test) and Tukey’s HSD for Total
Current Assets / Total Assets (TCA / TA)
C Results of ANOVA (F-test) and Bonferroni Test for Total
Current Assets / Total Assets (TCA / TA)
TABLE 4.3 A Results of ANOVA (F-test) and Test of Least Significant
Differences (LSD) for Total Current Liabilities / Total Assets
(TCL / TA)
B Results of ANOVA (F-test) and Tukey’s HSD for Total
Current Liabilities / Total Assets (TCL / TA)
C Results of ANOVA (F-test) and Bonferroni Test for Total
Current Liabilities / Total Assets (TCL / TA)
TABLE 4.4
Rank Order Correlations for Working Capital Management
Policies
TABLE 4.5
Year-wise Rank Correlation of Working Capital Management
Policies
TABLE 4.6 A Regression Analysis of Average Performance Measures &
Working Capital Investment Policy
B Regression Analysis of Average Performance Measures &
Working Capital Financing Policy
TABLE 4.7 A Year-wise Regression Analysis of Return on Assets (ROA) &
Working Capital Investment Policy
B
Year-wise Regression Analysis of Return on Assets (ROA) &
Working Capital Financing Policy
TABLE 4.8 A Year-wise Regression Analysis of Return on Equity (ROE) &
Working Capital Investment Policy
B
Year-wise Regression Analysis of Return on Equity (ROE) &
Working Capital Financing Policy
TABLE 4.9 A Year-wise Regression Analysis of Market Rate of Returns
(MRR) & Working Capital Investment Policy
B
Year-wise Regression Analysis of Market Rate of Returns
(MRR) & Working Capital Financing Policy
TABLE 4.10 A Year-wise Regression Analysis of Tobin’s Q & Working
Capital Investment Policy
B Year-wise Regression Analysis of Tobin’s Q & Working
Capital Financing Policy
TABLE 4.11 A Panel Data Regression Analysis of Performance Measures &
Working Capital Investment Policy
B
Panel Data Regression Analysis of Performance Measures &
Working Capital Financing Policy
TABLE 4.12 A Regression Analysis of Risk & Working Capital Investment
Policy
B
Regression Analysis of Risk & Working Capital Financing
Policy
3
43-45
49
50
51
52
53
54
55
56
58
59
62
63
64
65
66
67
68
69
70
71
73
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ACKNOWLEDGEMENTS
All praises to The Allah Almighty who has created this world of knowledge for us. He is The
Gracious, The Merciful. He bestowed man with intellectual power and understanding, and gave him
spiritual insight, enabling him to discover his “Self” know his Creator through His wonders, and
conquer nature. Next to all His Messenger Hazrat Muhammad (SAW) Who is an eternal torch of
guidance and knowledge for whole mankind.
Many individuals have been supportive and instrumental in assisting me with this work, and I owe
them a debt of gratitude. I am deeply thankful to my research supervisor, Dr. Talat Afza, whose
continuous guidance, feedback, advice and encouragements have been truly exceptional. It would
not be an exaggeration to say that if she had not been there, I may not have reached the finishing
line. I have learnt an enormous amount from her about conducting research as well as thinking
about new problems. I would appreciate Dr. Ahmad Kaleem, whose teaching methodology and doit attitude inspired me greatly and help me out. I would also like to say thanks to Dr. Mahmood
Ahmad Bodla, Chairman, who always has opened his doors to facilitate us and used to spend a lot
of time for sharing and discussing about the new ideas in research. Finally, it would not be justified
if I don’t mention the support of my fellows. Some of my friends who have played a vital role in the
completion of this work are Salahudin, Adnan, Jawad, Aniqa and Shakeel. All of these my friends
have been very encouraging and accommodating for me.
4
Chapter 1
INTRODUCTION
1.1 The Nature of Working Capital
In economics, capital is often used to refer to capital goods consisting of a great variety of things,
namely, machines of various kinds, plants, houses, tools, raw materials and goods-in-process. A
finance manager of a firm looks for these things on the assets side of the balance sheet. For capital,
he turns his attention to the other side of the balance sheet and never commits the mistake of adding
the two together while taking the census of total capital of the business. His purpose is to balance
the two sides in such a way that the net worth of the firm increases without increasing the riskiness
of the business. This balancing may be called financing, i.e. financing the asset of the firm by
generating streams of liabilities continuously to match with the dynamism of the former.
Although assets denote wealth of the firm, firms may not like to hold many of the assets appearing
on the balance sheet. While they may like to hold fixed assets like plant and machinery which
generate goods and services; the sales of which generate a profit; current assets like debtors,
inventories or even cash are not likely to be held in the business. The ideal situation for the firms is
where the production process takes very little time to convert the input to finished goods which gets
sold immediately in cash the moment it rills out of the process; and the input market is so perfect
that any amount of raw material is available at any time at a fixed price. However, this is a
visionary situation difficult to have. Instead, the production process takes quite some time; the
finished products are not sold so quickly which means a quantity of stocks remains in the godowns.
Moreover, the sales are not always in cash; some amount of credit has to be given and the input
markets are so uncertain that the firms to keep a certain amount of safety stock all the time. These
‘non-ideal’ conditions thus generate certain assets which are called current assets. These current
assets block the funds which should have been otherwise available for meeting working expenses.
Each and every current asset of a firm is, therefore, nothing but congealed fund for working
expenses. And because business is a continuous process, every cycle of operation generates these
current assets which need to be funded for immediate financing of working expenses. This funding
for working expenses is done by, what we popularly call, working capital.
1.2 Definition and Concept of Working Capital
The term working capital originated with the old Yankee peddler, who would load up his wagon
with goods and then go off on his route to peddle his wares (Brigham and Gapenski 1996). The
5
merchandise was called working capital because it was what he actually sold, or turned over, to
produce the profits. The wagon and horse were the fixed assets. The peddler generally owned the
horse and wagon so, they financed with equity capital. But, he borrowed the funds to buy the
merchandise. These borrowing were called working capital loan, they had to be repaid after each
trip to demonstrate to the bank that the credit was sound. If the peddler was able to repay the loan,
then the bank would make another loan, and banks that followed this procedure were said to be
employing sound banking practices.
The concept of working capital was, perhaps, first evolved by Karl Marx (1867), thought in a
somewhat different form. Marx used the term ‘variable capital’ meaning outlays for payrolls
advanced to workers before the goods they worked on were complete. He contrasted this with
‘constant capital’, which according to him, is nothing but ‘dead labor’, i.e. outlays for raw materials
and other instruments of production produced by labor in earlier stages which are now needed live
labor to work with in the present stage. This ‘variable capital’ was the wage fund which remains
blocked in terms of financial management, in work-in-process along with other operating expenses
until it is released through sale of finished goods. Although Marx did not mention that workers also
gave credit to the firm by accepting periodical payment of wages which funded a portion of workin-process, the concept of working capital, as we understand today, was embedded in his ‘variable
capital’.
The working capital of a business enterprise can be said as portion of its total financial resources
which is put to a variable operative purpose. The facilities that are necessary to carry on the
productive activity and represented by fixed asset investment (i.e. non-current asset investment) are
to be operated by working capital. Van Horne and Vachowicz (2004) defined the working capital
management as that aspect of financial activity that is concerned with the "safeguarding and
controlling of the firm's current assets and the planning for sufficient funds to pay current bills." In
an annual survey of industries, the working capital is defined as "stocks of raw materials, stores,
fuels, semi finished goods, including work in progress and finished products; cash in hand and at
the bank and the algebraic sum of sundry creditors as represented by (a) outstanding factor
payments e.g. rent, wages, interest and dividends; (b) purchase of goods and services; (c) short term
loan and advances and sundry debtors comprising amounts due to the factory on the account of sale
of goods and services and advances towards tax payments."
But with the evolution of the concept came the controversy about the definition of working capital.
Guthman and Dougall (1948) defined working capital as excess of current asses over current
6
liabilities. This view was elaborated by Park and Gladson (1951) when they defined working capital
as the excess of current assets of a business (cash, accounts receivables, inventories, for example)
over current items owed to employees and others (such as salaries and wages payables, accounts
payables, taxes owed to government). This concept of working capital, as has been commonly
understood by the accountants, more particularly understood as net working capital to distinguish it
from gross working capital which is discussed later. Walker (1974) holds that this concept is useful
to groups interested in determining the amount and nature of assets that may be used to pay current
liabilities. These interested groups, as suggested by Walker, mostly composed of creditors,
particularly the supply creditors who may be concerned to know the ‘margin of safety’ available to
them should the realization of current assets be delayed for some reasons.
1.3 Working Capital Management
Although economists regard fixes capital as what is represented by long-term assets, a finance
manager defines fixed capital as that having long term maturity. It is not essential to restrict
utilization of the fixed capital to finance fixed asset only; rather, to use the fixed capital to finance a
part of current assets also in addition to financing fixed assets is preferred. There may also be a
situation where all the fixed and current assets are financed from fixed capital only. In the latter
case, the firm will have current assets but no current liability, but we cannot say that the firm does
not have any need for working capital. The firm might not desire to contract current liability, but its
operations would have generated current assets which have to be funded to ensure continuity of
production. This fund is, in fact, an additional fund over and above the fund required to meet
working expenses of the firm.
Management of current assets is, therefore, distinct from the management of fixed assets. But more
important than this tautology is that because of the dynamism of the current assets, the finance
manager has to be constantly on guard to ensure that their dynamic stability is not impaired to affect
the net worth of the firm negatively. Gross current assets should, therefore, be understood by their
own meaning, connotation and effect on the firms and should not mechanically equated with gross
working capital just because arithmetically the two may appear to be same in a balance sheet.
Besides, the funding operations of the current assets are quite distinct from the management of
current assets. A modern day finance manager first projects the level of current assets of the firm
under projected sales and then tries to find out sources to finance these current assets in such a way
that cost of capital is optimized. Management of gross current assets and gross working capital, or
simply working capital, is not one and the same. The total of projected current assets is an aggregate
7
figure for which capital has to be raised and the two may not have any bearing on each other. The
profiles of the types of capital so raised in regard to the risk opportunity for gain or loss and also the
cost are different from that of current assets.
Working capital is so much in use in common parlance and is so much misunderstood. Even among
the professional managers the controversy and confusion persist. While an accountant will regard
working capital as current assets minus current liabilities and call it as net working capital, a finance
manager will consider gross current assets as the working capital. Both may be true, but their
concerns differ. The former’s concern is arithmetical accuracy trained as he is to tally the two sides
of the balance sheet. But the finance manager’s concern is to find fund for each item of current
assets at such costs and risks that the evolving financial structure remains balanced between the
two. When one asks a production controller: what is working capital? His answer is very simple and
straightforward that working capital is the fund needed to meet the day-to-day working expenses,
i.e. to pay for materials, wages and other operating expenses. Is there any difference between the
statements of ultimate analysis; the latter may be true, but according to the accountant or the finance
manager it is the very working expenses that get blocked in current assets along the productivedistributive line of an enterprise, and net working capitals that liquidity which takes care of the
working expenses if the line gets extended due to any reason.
However, the notion of the liquidity itself has undergone considerable changes with the advances in
financial management during the recent years. Liquidity has so far been defined as a pyramid of
current assets in descending order of realisability with cash holding the top position and inventory,
the last. This notion has given rise to liquidity ratios such as current ratio or quick ratio, and later to
the concept of net working capital. The pyramid is no upside down with inventory at the top. When
we examine the pipeline theory of working capital, we will find that pipeline of the productivedistributive system of an enterprise consists of only inventories which, at different stages, take on
different names like work-in-progress, finished goods, accounts receivable, cash balance, etc.
Working capital structure is being so designed today in efficient organizations as to take care of this
fundamental liquidity of an enterprise with zero or even negative net working capital.
Firms may have an optimal level of working capital maximizes their value. Large inventory and a
generous trade credit policy may lead to high sales. Larger inventory reduce the risk of the stockout trade credit may stimulate sales because it allows customers to assess product quality be for
paying (Long et al. 1993) and (Deloof and Gegers, 1996). Because suppliers my have significant
cost advantages over financial institutions in providing credit to there customers, it can also be an
8
inexpensive source of credit for customer (Peterson and Rajan, 1997). The flip side of granting
trade credit and keeping inventories is that money is locked up in working capital.
1.4 Working Capital Management and the Researchers’ View
The corporate finance literature has traditionally focused on the study of long-term financial
decisions. Researchers have particularly examined investments, capital structure, dividends or
company valuation decisions, among other topics. However, short-term assets and liabilities are
important components of total assets and needs to be carefully analyzed. Management of these
short-term assets and liabilities necessitates a careful investigation since the working capital
management plays an important role for the firm’s profitability and risk as well as its value (Smith,
1980). Firms try to keep an optimal level of working capital that maximizes their value (Howorth
and Westhead 2003, Deloof 2003).
In general, from the perspective of Chief Financial Officer (CFO), working capital management is
simple and a straightforward concept of ensuring the ability of the organization to fund the
difference between the short term assets and short term liabilities (Harris 2005). However, a “Total”
approach should be followed which cover all the company’s activities relating to vendor, customer
and product (Hall 2002). In practice, working capital management has become one of the most
important issues in the organizations where many financial executives are struggling to identify the
basic working capital drivers and the appropriate level of working capital (Lamberson 1995).
Consequently, companies can minimize risk and improve the overall performance by understanding
the role and drivers of working capital.
Working capital management policies have been divided into two categories by Weinraub and
Visscher (1998). A firm may adopt an aggressive working capital asset management policy with a
low level of current assets as percentage of total assets. On the other hand, aggressive working
capital financing policy uses high level of current liabilities as percentage of total liabilities.
Excessive levels of current assets may have a negative effect on the firm’s profitability whereas a
low level of current assets may lead to lower level of liquidity and stock-outs resulting in
difficulties in maintaining smooth operations (Van Horne and Wachowicz 2004). Moreover,
aggressive working capital financing policies that utilize higher levels of normally lower cost shortterm debt increase the risk of a short-term liquidity problem. Therefore, more aggressive working
capital policies are associated with higher return and higher risk while conservative working capital
policies are concerned with the lower risk and return (Carpenter and Johnson 1983, Gardner, Mills
and Pope 1986, Weinraub and Visscher 1998).
9
The main objective of working capital management is to maintain an optimal balance between each
of the working capital components. Business success heavily depends on the ability of financial
executives to effectively manage receivables, inventory, and payables (Filbeck and Krueger 2005).
Firms can reduce their financing costs and/or increase the funds available for expansion projects by
minimizing the amount of investment tied up in current assets. Most of the financial managers’ time
and effort are allocated in bringing non-optimal levels of current assets and liabilities back toward
optimal levels (Lamberson 1995). An optimal level of working capital would be the one in which a
balance is achieved between risk and efficiency. It requires continuous monitoring to maintain
proper level in various components of working capital i.e. cash receivables, inventory and payables
etc.
The optimal level of working capital is determined to a large extent by the methods adopted for the
management of current assets and liabilities. It requires incessant management to maintain proper
level in various components of working capital i.e. cash receivables, inventory and payables etc. In
general, current assets represent important component of total assets of a firm. A firm may be able
to reduce the investment in fixed assets by renting or leasing plant and machinery, whereas, the
same policy cannot be followed for the components of working capital. The high level of current
assets may reduce the risk of liquidity associated with the opportunity cost of funds that may have
been invested in long-term assets. The impact of working capital policies is highly important,
however as per the available literature, no empirical research has been carried out to examine the
impact of working capital policies on profitability and risk of firm. These profitability assumptions
suggest maintaining a low level of current assets and a high proportion of Current liabilities to total
liabilities. This strategy will result in a low, or conceivably negative, level of a working capital.
Offsetting the profitability of this strategy, however, is the increased risk to the firm. In determining
the appropriate amount, or, level, of current assets, management must consider the trade- off
between profitability and risk.
Many surveys have indicated that managers spend considerable time one-day-to-day problems that
involve working capital decisions. One reason for this is that current assets are short-lived
investment that are continually being converted into other assets types (Rao 1989). For example,
cash his used to purchase inventory items eventually become accounts receivable when they are
sold on credit; and finally, the receivable are transformed into cash when they are collected. With
regard to current liabilities, the firm is responsible for typing these obligations on a timely basis.
The ability to match short term obligations has only improved from a liquidation perspective and
10
not from a going concern approach (Shulman and Dambolena, 1986).
A firm’s net working capital is also often used as a measure of its liquidity position. That is, it
represents the risk or probability that a firm will be unable to meet is financial obligations as they
come due. Therefore, the more net working capital a firm as, the greater its ability to satisfy credits,
demands. Moreover, because net working capital serves as an illiquid risk measure the firm is net
working capital position will affect its ability to acquire debt financing. For example, commercial
banks often impose minimum working capital constraint in their loan agreements with firms
similarly; bond indentures may contain such restrictions. Due the credit squeeze, the problem of
working capital management has acquired special importance. The leading policies of commercial
banks have under revolutionary change in the recent periods. The shift in the emphasis from
security to purpose of advance has affected a large number of borrowers. Beside this, norms for
inventory and debtors have also been laid down. To aim at a sense of discipline in the working
capital management all these developments have been introduce.
The level and nature of business firm investment depend on the firms’ product types, its operating
expenses, and management policy. As sales increase over time, more cash, receivable, and
investment with usually are needed. Even within a firm’s normal operating cycle, seasonal sales
patterns cause the level of current assets to be relatively high or low at any particular point.
Moreover, the firms’ credit and inventory policies, and how efficiently it manages its current assets,
can drastically affect a firms working capital need. For example, a conservative toy manufacturer
may maintain a high level of inventory to satisfy unexpected demands or to hedge against delays in
acquiring new inventory. A more aggressive or more efficient toymaker, on the other hand, may
function with a much lower investment in inventories.
Current assets provide the liquidity necessary to support the realization of the expected returns from
firms’ long-term investments. The cash flows associated with long-term investments are uncertain
and irregular, and it is the non synchronous nature of the cash flows that makes working capital
necessary. Otherwise, a mismatch between cash inflows and outflows could a liquidity crisis. This,
in turn, could disrupt or reduce the long-term returns expected from a firm's fixed asset investment.
Current assets therefore act as a buffer to reduce the mismatch between cash outflows for goods and
services and the cash receipts generated by the sales revenues.
11
1.5 Significance of the Management of Working Capital
For the success of an enterprise proper management of working capital is very important. It aims at
protecting the purchasing power of assets and maximizing the return on investment. The success of
operations of a firm is determined to a large extent by the method administration of its current. It
requires continuous management to maintain proper level in various components of working capital
i.e. cash, receivables and inventory etc. In establishing proper proportions, cash and financial
budget may be very useful. Sales expansion, dividend declaration, plant expansion, new product
line, increased salaries and wages, rising price levels etc. put added strain on working capital
maintenance. Due the poor management and lack of management skills, business fails certainly.
Shortage of working capital, so often advanced as the main cause of failure of industrial concerns, is
nothing but the clearest evidence of mismanagement which is so common.
The prime object of management is to make profit. This accomplishment in most business depends
largely on the manner in which their working capital management usually is considered to involve
the administration of current assets of a company namely, cash, account receivables and inventory.
Administration of fixed assets falls within the realm of capital budgeting while the management of
working capital is a continuing function which involves control of every day and flow of financial
resources circulating in the company in one form or the other. In turn these decisions are influenced
by the trade- off that must be made between profitability and risk. Lowering the level of investment
in current assets, while still being able to support sales, would led to an increase in the firm is return
on total assets. Smith (1980) first signaled the importance of the trade offs between the dual goals of
working capital management, i. e., liquidity and profitability. To the extent that the explicit costs of
short-term financing are less then those of intermediate and long-term financing, the greater the
proportion of short-term debt to total debt, the higher is the is the profitability of the firm. From
another perspective, the current assts of a typical manufacturing firm accounts for ever half of its
total assets. For a distribution company, they account for even more. Excessive levels of current
assets can easily result in a firm is realizing a substandard return on investment. However firms
with too few current assets may incur shortages and difficulties in maintaining smooth operations
(Van-Horne and Wachowicz, 2004).
For small companies current assets and liabilities are the principal source of external financing.
These firms do not have access to the longer term capital markets, other than to acquire a mortgage
on a building. The fast growing but a larger company also makes use of current liabilities financing.
For these reasons the financial manager and staff devotes a considerable portion of their time to
12
working capital matters. The management of cash, marketable securities, receivable, payables,
accruals, and other means of shot-term financing is the direct responsibility of a financial manager,
only the management of inventories is not. Moreover, these management responsibilities require
continuous day to day supervision. Unlike dividend and capital structure decision, one cannot study
the issue, reach a conclusion, and set the mater aside for many months to come. Thus working
capital management is important, if for no other reason then the proportion of the financial
managers’ time that must be devoted to it. More fundamental working capital management
decisions have its effects on the company is risk return, and share price.
While managing working capital, two fundamental decisions the firm must be taken into the
consideration are: Firstly, how much to invest in the current assets to have an optimal mix of current
and fixed assets. Secondly, an appropriate mix of short-term and long-term financing options used
to support the investments in current assets must be looked into. In turn, the above discussed tow
decisions are influenced by the trade off that must make between profitability and risk. Return on
assets can be increased by lowering the level of investment in current asset, while still being able to
support sales.
1.6 Research Objectives
The present study investigates the relationship of the aggressive and conservative working capital
asset management and financing polices and its impact on profitability of 204 Pakistani firms listed
on Karachi Stock Exchange for a period of 1998-2005. This research:
•
Examines whether significant differences exist among the working capital practices of
the firms across different industries,
•
Confirm whether these aggressive or conservative working capital policies are relatively
stable over the period of time,
•
Validates the relationship between working capital asset management and financing
policies firms and investigates how a working capital asset management policy
corresponds to working capital financing policy.
•
The impact of aggressive and conservative working capital asset management and
financing policies profitability of the company by using various profitability measures
based on accounting data and market values.
13
•
Finally, to confirm the findings if Carpenter and Johnson (1983), the study has
investigated the impact of working capital policies on the financial and operating risk of
firms operating in Pakistan.
Therefore, it is hoped that this study will contribute to better understand these policies and their
impact especially in the emerging markets like Pakistan. The remaining portion of the document is
organized as follows: Chapter two reviews the relevant literature for the theoretical and empirical
work on working capital management and its effect on profitability. Chapter three presents the
research methodology and frame work which includes sample and the variables used for the
analysis purpose. Chapter four portrays and discusses the data analysis inferred by applying some
statistical techniques. Finally, Chapter five deals with the conclusion and recommendations the
study reports.
14
Chapter 2
LITERATURE REVIEW & HYPOTHESES
Many researchers have focused on financial ratios as a part of working capital management;
however, very few of them have discussed the working capital policies in specific. Some early work
by Gupta (1969) has focused on exploring the relationship between industries, size and growth
effect on the financial structure of American manufacturing companies for a period of 1961-62. A
large sample of 173,000 manufacturing corporations covering 21 SIC two-digit industries, classified
into 13 categories according to their size were examined. For the purpose of analysis, four
categories of financial ratios i.e. profitability, activity, leverage and liquidity were used. By
applying the Spearman’s rank correlation, the study concluded that firms with small size and high
growth tend to have lower liquidity ratios whereas large firms with lower growth rates might have
high liquidity ratios. The patterns of financial ratios were different across various industry groups
and were tend to be stable over a shorter period of time of 1961-1962.
In a subsequent study, Gupta & Huefner (1972) used hierarchical cluster analysis to examine the
descriptive or representative power of financial ratios with the basic industry attributes. The study
covered twenty manufacturing industries as classified by Income Statistics data published by the
Interval Revenue Services. For each industry, several financial ratios were calculated and clusters
analysis was applied to those ratios. The study reported that differences in financial ratios do exist
across the different industries. Moreover, assets ratios were related to industry characteristics both
in terms of individual industries as well as industry groups. However, when aggregate assets
turnover ratios were used, the inter-industry differences tend to disappear.
The preliminary work on financial ratios has been done by Pinches et al. (1973) who developed and
tested the empirical classifications of financial ratios as well as the stability of ratio groups over the
longer period of time from 1951 to 1969. The sample included 221 firms from the various industrial
sectors of United States and forty-eight financial ratios were calculated for the classification
purpose, which were further normalized through obtaining common log transformation of financial
ratios. The study used R factor analysis to classify financial ratios into similar groups whereas
differential R factor analysis was employed to determine the degree of stability over the longer time
period. The results of R factor analysis yielded seven classifications of financial ratios for the
sample firms; return on investment, capital intensiveness, inventory intensiveness, financial
leverage, receivable intensiveness, short-term liquidity and cash position. Furthermore the results of
15
differential R factor analysis suggested that the composition of these financial ratios into seven
classifications were reasonably stable over the longer period of study.
Another perspective of financial ratios was studied by Walker & Petty (1978) by analyzing the
differences among the financial management policies and practices of 31 large and 31 small public
firms in United States from the different industrial sectors. Five dimensions of financial operations
of a firm were analyzed to distinguish between large and small firm financial management
practices; liquidity, profitability, financing, business risk and dividend policy indicators. The
statistical methods employed were F-test and multivariate discriminant analysis. The study
concluded that small and large corporations do have different financial management characteristics.
Moreover, small size firms can earn more profits than large corporations because of the efficient
management of firm’s assets. The larger firms have greater liquidity than smaller firms due to
greater access to the financial markets and wide variety of available financing options.
Johnson (1979) extended the work of Pinches et al. (1973) and Pinches et al. (1975) by finding the
cross sectional stability of financial ratios for primary manufactures and retailers in U.S. The
research was based on sixty-one financial ratios calculated using the annual financial data for a
period of 1972 to 1974. In addition to seven classifications of financial ratios found by Pinches et
al. (1973) study, the principal component analysis identified decomposition measures factor, which
included assets decomposition, equity decomposition and non-current items decomposition of
balance sheet. Moreover, canonical co-relational analysis confirmed that the eight factors of
financial ratios possessed a high degree of cross-sectional stability across the two industries for the
period of study.
Chen and Shimerda (1981) presented a summary of the financial ratios used in a number of early
studies, which used the financial ratios for analysis and prediction purposes. It is been noted by the
authors that there was an abundant 41 different financial ratios, which were found useful in the
earlier studies. They reconciled the factors in the earlier studies into financial leverage, capital
turnover, return on investment, inventory turnover, receivables turnover, short-term liquidity, and
cash position and identified ten financial ratios which were representative of their seven factors.
After a principal component factor analysis of 39 ratios of the Pinches et al. (1975), the study
concluded that there was a high instability in always selecting the financial ratio with the highest
absolute factor loading as the representative financial ratio for the observed factors.
16
Furthermore, Scott & Johnson (1982) provided an insight into the financing policies of large
American corporations. The data was collected through 25-items questionnaire mailed to chieffinancial officers of Fortune 1000 firms. The usable and complete questionnaires were returned by
212 CFOs with a response rate of 21.2%. The results indicate that 90% of the respondents used
financial leverage ratios to make the financing decisions. The most important ratios ranked by
CFO’s were long-term debt to total capitalization ratio, times interest earned and long term debt to
net worth whereas the firms used book values for computing the ratios rather market values.
Moreover, 53% of the respondents confirmed that industry benchmarks influence their decisions of
financing choices.
In the continuity of earlier studies, Gombola & Ketz (1983) analyzed 783 manufacturing and 88
retail firms to examine the intra-industry stability of financial ratio patterns for a period of ten years
from 1971 to 1980. The set of 58 financial ratios studied by Pinches et al. (1973) was re-examined
in the study. Classification patterns among financial ratios were developed with the help of factor
analysis varimax rotation procedure both for retailing and manufacturing firm sectors for each of
the ten years of study. The findings of Pinches et al. (1973) were supported with two additional
financial ratios factor i.e. cash expenditures and cash flow. Moreover, the study also found that both
the retailing and manufacturing firm sectors exhibited cross-sectioned stability across years as well
as time series stability within industries.
The study of Pinches et al. (1973) has been replicated in Europe by Ezzamel et al. (1987) who
reported the empirical evidence on the types and the extent of long-term stability in the financial
patterns for U.K. manufacturing companies. The data about the financial statements of 1434
manufacturing companies for a period of 1973-1982 has been used for the study. The principal
component factor analysis identified five broad categories of financial performance of UK
companies; total fund structure, profitability, working capital position, liquidity and assets turnover.
The identified financial patterns were found to be partly stable over the study period as per the
results of differential R factor analysis except working capital position, which was significantly
stable at higher level of significance.
Pandey and Bhat (1990) provided regional evidence on empirical classification of financial ratios
and examined the intertemporal stability/change of classifications of financial ratios in Indian
manufacturing companies. Factor analysis, differential R factor analysis, and correlation percentage
absolute deviations have been used as statistical methods. The study found eleven financial ration
factors i.e. return on investment, sales efficiency, equity intensiveness, short-term liquidity, current
assets intensiveness, cash position, activity, earnings appropriation, financial structure, interest
17
coverage, and long-term capitalization. Furthermore, differential R factor analysis confirmed that
these eleven financial ratios patterns were reasonably stable over the period of study.
In addition to the study of Gombola & Ketz (1983), Chu et al. (1991) analyzed acute-care hospitals
to explore whether the hospital financial ratio groups differ from industrial firm financial ratio
groups and whether these groups remain stable over the period of study. The financial data about
113 acute-care hospitals was use for a period of five years from 1983 to 1987. Varimax rotated
factors analysis has been used to classify 31 selected financial ratios into independent groups. The
results identified seven industrial ratio groups as with those of industrial firms, however, a
consistently independent factors of working capital flow indicated that hospital working capital
measure do differ from other industrial firms. Furthermore, the eight ratio groups did not remain
stable throughout the study period. The reason for this unstable pattern of financial ratios might be
due the impact of financial constraints on hospital finances. The Medicare Prospective Payment
System was gradually implemented during the period of study, therefore, financial ratio patterns did
not remained stable over the period.
Martikainen and Ankelo (1991) reported empirical evidence on the relationship between the
stability of financial ratio patterns and corporate failure of firms operating in an emerging market of
Finland. The study used the data of 20 failed and 20 non-failed firms for a period of five years
before actual failure and twelve financial ratios were used for the analysis purpose i.e. Net Income
to Sales, Return on Investment, Return on Assets, Quick Ratio, Current Ratio, Quick Assets to
Liabilities, Equity to Capital, Debt to Sales, Net Interest to Sales, Receivable Turnover, Inventory
Turnover and Payable Turnover. Principal component factor analysis with varimax rotation has
been used to identify the financial patterns of failed and non-failed firms whereas relative stability
of these financial patterns in failed and non-failed firms was found by applying transformation
analysis. The results indicated that the financial patterns of failed firms were unstable than those of
non-failed firms and this unstable pattern was observed from the beginning of the study period i.e.
five years before the actual failure of firms.
In their later study, Martikainen et al. (1994) applied transformation analysis to compare the
financial ratio structure between industries for 74 failed and 74 non-failed firms in Finland. The
industry effects on financial ratios were investigated by using 16 financial ratios for small and
medium sized Finnish firms. Four ratio classifications were found for three industries through
principal component analysis and varimax rotation. The results indicated that there was a significant
industry effect on the classification patterns of financial ratios. Furthermore, this industry effect was
18
stronger for working capital ratios and efficiency ratios and this relationship remained significant in
both small and large Finnish firms.
In literature, there is a long debate on the risk/return tradeoff between the different working capital
policies. More aggressive working capital policies are associated with higher return and higher risk
while conservative working capital policies are concerned with the lower risk and return. In this
regard, Belt (1979) highlighted the determinants of working capital policies and the rewards & risks
of an aggressive working capital policy. The author claimed that there might be two real
determinants of the level of net working capital of a firm. The first determinant is the operational
aspect that describes how much liquidity is needed in the current assets of the firm whereas the
second determinant is the deferability of current liabilities of the company, which is the ability to
postpone or delay the payments to creditors for some period. The author also asserted that the
rewards and risks of an aggressive working capital policy vary between industries and within
industries. Generally, the benefits might be attained by reducing the holding costs of fast moving
inventory, lower collection costs associated with lower level of receivables and somewhat less
costly short-term debt. However, higher returns of an aggressive working capital policy are often
associated with risk of liquidity as well. The use of large level of current liabilities, i.e. aggressive
financing policy of working capital increases the chances of financial embarrassments that can lead
to corporate failure and bankruptcy.
Based on the theory of Belt (1979), Nunn (1981) explored empirically the various strategic
determinants of permanent working capital level on product line perspective. By applying the crosssectional of regression analysis, the study developed a model consisting of strategic determinants
that explain why working capital differs from business to business. The sample consisted of 400
diverse businesses for a period of eight years from 1971-1978. Principal component and varimax
rotation factor analysis was applied to identify 166 explanatory variables of working capital
requirements for different businesses. A multiple regression model was built on 43 independent
factors identified by factor analysis and dependant variable i.e. working capital to sales, working
capital level is influenced by production, selling and accounting related variables as well as industry
characteristics.
In addition, Carpenter & Johnson (1983) empirically investigated the relationship between the level
of current assets and the operating risk of firms. The sample firms included were the large firms
from various industrial sectors in the United States. The sample data was collected for a period of
four years i.e. 1978-1982. The regression analysis showed no significant linear relationship between
the level of current assets and revenue systematic risk or operating systematic risk. However, some
19
indications of a possible non-linear relationship were found which were not highly statistically
significant.
A primary research by Lamberson (1989) provided an insight into the importance and utilization of
financial analysis and working capital management by small manufacturers in U.S. A questionnaire
was mailed to chief financial officers of 477 small firms in the southern region of U.S. The 85% of
the respondents indicated that they use ratio analysis for financial planning on monthly basis.
Working capital management was ranked important and most important by 90% of the respondents.
The importance of working capital management for small firms was, perhaps, due to their limited
access to capital markets and finance providers. Accounts receivables and inventory management
among the various components of working capital were ranked the most important while cash
management was ranked least important by small manufacturers.
Gentry et al. (1990) introduced a new concept of weighted cash conversion cycle in order to
examine the working capital management of firms. The weighted cash conversion cycle measures
the weighted number of days the funds are tied up in the accounts receivables, accounts payables
and inventories less number of days payments are deferred to suppliers of the firms where the
weights are calculated by dividing the amount of cash tied up in each component of working capital
by the final value of product. The proposed weighted cash conversion cycle is an aggregate measure
of the amount and speed that the funds flow through working capital accounts of a firm and
concentrates on the real resources committed to the total working capital process.
Belt & Smith (1991) compared the working capital management practices of Australia and United
States with two earlier surveys conducted in United States by Smith & Sell (1980) and Belt and
Smith (1989). A mail questionnaire has been used to collect the data from 144 Australian firms
selected from Australian Business “Top 500 Companies”. The final response was received from
thirty-nine firms, which were categorized on the basis of size and profitability with a response rate
of 27.1%. The results indicated that about 95% of the respondents had a formal or informal working
capital policy as in the previous surveys. Two-third of the firms managed working capital on
situational basis rather than risk-avoiding or risk-accepting factors, which was 28% only. The most
important factors of working capital management in Australia and United States was speeding the
collection of receivables and minimizing investing in inventory. Moreover, the working capital
management in Australia was found to be more centralized than United States. On average, the
results were consistent with previous stories of Smith & Sell (1980) and Belt & Smith (1988).
20
Kim et al. (1992) dealt with two issues in their study; what were the objectives of working capital
management by Japanese manufacturers in the United States and; to identify the major options of
short-term funds. The necessary data was obtained through a detailed questionnaire mailed to
financial managers of 326 Japanese manufacturers operating in the United States. The received
response was about 94 firms with a response rate of 29%, which was quite reasonable. The data
indicated that most important objective of working capital management for Japanese firms in US
was to provide the current assets and liabilities to support the expected sales. The second most
important objective was to evaluate changes in investment decisions in current assets and to
minimize the cost of short-term credit. More than 70% of times, Japanese firms used outside
financing as major source of short term financing. About 90% of the respondents indicated that they
prefer financing either from Japanese banks in U.S or parent company in Japan which is a strong
indication of Japanese Firm’s involvements in “Kieretsu” which indicate the close link of Japanese
firms with their parent firms.
For the first time, Soenen (1993) investigated the relationship between the net trade cycle and return
on investment in U.S firms. The study also checked the impact of cash conversion cycle across
industries on the profitability of firms. The annual financial data for 2000 firms and 20 industries
had been used for analysis purpose for the period of 1970-1989. The firms were divided into four
quadrants on the basis of the median values of net trade cycle and return on assets. A chi-square test
was applied to measure the association between the quadrants of firms, which indicated negative
relationship between the length of net trade cycle and return on assets. Furthermore, this inverse
relationship between net trade cycle and return on assets was found different across industries
depending on the type of industry. A significance relationship for about half of industries studied
indicated that results might vary from industry to industry.
Empirical evidence on traditional trade credit theories has been presented by Long et al. (1993)
using a large sample of US manufacturing firms. The data has been collected for three years i.e.
1985-1987 of all industrial firms with SIC Code 2000 through 3999. Using univariate and pooled
cross-sectional regression analysis, the results supported the product quality theory that smaller
firms with longer production cycle extend more credit than larger firms having superior quality of
products. The evidence also suggested that these practices of extending trade credit varied both
within and across industries. Moreover, the results indicated that extending more credit might be
financed through short-term borrowings and payables.
21
The financial management techniques employed by large firms of Fortune 500 corporations of 1991
have been surveyed by Gilbert and Reichert (1995). Chief financial officers of 151 companies
completed and returned the questionnaires with a quite reasonable response rate of 30.2%.
Comparing the responses with another survey of fortune 500 firms in 1980 and 1985, the authors
concluded that majority of the firms used financial management and working capital techniques for
their financial planning. Accounts receivables management and inventory management models
were adopted by almost 60% of the respondents as sophisticated working capital management
techniques. Furthermore, the usage of these working capital management techniques has been
increased in 1991 as compared to 1980 and 1985.
Lamberson (1995) studied empirically how small firms respond to changes in economic activities
by changing their working capital positions and level of current assets and liabilities. Small firms
were expected to increase the level of current assets and liabilities as the economy expands. The
hypothesis was tested on 50 small firms in U.S. and the data about financial statements and
economic activity was collected from Moody’s industrial manual for a period of 1980-1991.
Current ratio, current assets to total assets ratio and inventory to total assets ratio were used as
measure of working capital while index of annual average coincident economic indicator was used
as a measure of economic activity. Correlation analysis was conducted to check the relationship
between the economic expansion/contraction and the level of working capital while the significance
of relationship has been measured by t test. Contrary to the expectations, current ratio and quick
ratio increased during expansion period while the other two ratios remained relatively stable during
the period of economic expansion. Overall, the study found that there is very small relationship
between charges in economic conditions and changes in working capital.
Rafuse (1996) analyzed the different aspects of optimal working capital management and its
components. His article argued that attempts to improve working capital management by delaying
the payments to creditors is an inefficient and ultimately damaging practice, both to its practitioners
and to economy as a whole. The study claimed that altering debtors and creditors levels would
rarely produce any net benefit rather it will harm the sales or the financing options of the firm. The
study proposed that stock reduction strategies based on some “Lean Production” techniques might
be far more effective than any other single working capital management technique. Reducing stock
would produce major financial advantages by improving cash flows, reducing operational level
costs of inventory and reducing capital spending. Moreover, it is further argued that the “Lean”
world-class companies are systematically better than their counterparts in every important aspects
and characteristics that makes a company “Lean” is low stock levels.
22
In order to validate the results found by Soenen (1993) on large sample and with longer time period,
Jose et al. (1996) examined the relationship between aggressive working capital management and
profitability of US firms. Cash conversion cycle (CCC) has been taken as a measure of working
capital management where a shorter cash conversion cycle represents the aggressiveness of working
capital management whereas pre-tax return on assets and equity has been used as profitability
measures. The data has been collected for a period of twenty-four years from 1974 through 1993 for
2,718 firms. The relationship between the cash conversion cycle and profitability measures has been
tested through cross-sectional regression analysis. The results indicated a significant negative
relationship between the cash conversion cycle and profitability indicating that more aggressive
working capital management is associated with higher profitability. The shorter the cash conversion
cycle, greater the return on assets and return on equity. Peel & Wilson (1996) surveyed 250 small
U.K. manufacturing and service firms to look into the working capital and financial management
practices. A questionnaire was mailed to managing directors of small firms located in north & west
of Yorkshire in 1993. The response of 84 companies indicated that, on average, 80% of small U.K.
firms were using the quantitative working capital and financial management techniques to manage
the companies’ assets.
A regional study by Pandey and Parera (1997) provided an empirical evidence of working capital
management policies and practices of the private manufacturing companies in Sri Lanka. The
information and data was collected through questionnaires and interviews with chief financial
officers of a sample of manufacturing companies listed at the Colombo Stock Exchange. The
authors concluded that most companies in Sri Lanka have informal working capital policy and the
managing director plays a major role in formulating formal or informal policy for working capital
management. Moreover, company size has an influence on the overall working capital policy
whether it is formal or informal and approach (conservative, moderate or aggressive). Company
profitability and working capital policy influenced the payable management and working capital
finance respectively. Current and cash budget are major techniques of working capital, planning and
control. Company profitability has an influence on the methods of working capital planning and
control. The sample companies considered sometimes working capital changes when they evaluate
capital budgeting decisions. Most of the studied companies used bank interest rate as a hurdle rate
for evaluating the working capital changes. Furthermore, a comparison of the working capital
practices of the Sri Lankan manufacturing companies with the USA manufacturing companies
revealed a lot of similarities. The basic difference is in terms of the use of computerized system and
the opportunity to invest surplus cash in the money market instruments.
23
Smith & Begemann (1997) examined the trade off between the liquidity and profitability of firm by
investigating the association between return on investment and alternative measures of working
capital. The study used the data bank of Bureau of Financial Analysis for 135 firms listed in
Johannesburg Stock Exchange (JSE) for a period of 1984 to 1993. By applying the Pearson Chisquare test, the study found significant positive association between accounts receivables, accounts
payables and inventory turnovers with return on investment. The step-wise regressions analysis
produced R2 of 52% and corroborated the finings of chi-square test. The alternative measures of
working capital management exhibited strong negative correlation with return on investment as 5%
level of significance.
Bhattacharyya and Raghavacahari (1997) examined the determinants of effective working capital
management in 72 large Indian companies. A questionnaire was mailed to the mangers to view the
perceptions in their working capital management process in their respective organization. The
Discriminant analysis indicated that the prime determinants of effectiveness of working capital
management in order to their relative importance were: 1) profit after tax as percentage of sales, 2)
sales as number of times to total assets, 3) quick assets as percentage of current liabilities and 4)
receivables as numbers of day’s sales. The authors recommended that the financial managers and
analysts should pay more formal and explicit attention to these four factors while conducting their
financial analysis.
Continuing with their early attempt, Shin & Soenen (1998) empirically examined the relationship
between efficiency of working capital management and the corporate profitability of firms by
taking net trade cycle as a measure of working capital management. The study used 58,985 firm
years record for a period of 20 years from 1975 to 1994. The Pearson linear and Spearman rank
correlation analysis indicated a significant inverse relationship between the net trade cycle and
accounting measure as well as market measure of profitability. The pooled and cross sectional
regression analysis confirmed the results of correlation analysis that firms with shorter trade cycle
earned more profits. However, the negative relationship between the current ratio and profitability
indicate a trade off between liquidity and profitability of a firm. Therefore, firms can improve
corporate profitability by enhancing efficiency of working capital management by keeping in view
the liquidity of firms. Furthermore, regression analysis also confirmed the impact of industry on a
firm’s investment in working capital. The results confirmed the findings of Hawawini et al. (1986)
that industry benchmarks must have been adhered by firms when setting their working capital
policies.
24
Maxwell et al. (1998) compared the short-term financial management practices of U.S firms with
previous study conducted by Gitman et al. (1979). The objective of the paper was to find the
similarities and differences between the two surveys as well as the financial management practices
of U.S and Non-US Firms. A 25-item questionnaire was mailed to chief financial officers of a
stratified sample of 2075 firms in 16 nations of European Union, North America and Pacific
Region. The response rate was very low i.e. 6.4% as only 133 questionnaires were returned.
Analysis has revealed a significant change in the short-term financial management practices since
1979 in U.S. Firms. Moreover, the study also suggested that significant differences exist in the
techniques and polices of short-term financial management among the U.S firms and foreign firms.
The differences between 1979 survey and current study as well as between domestic and foreign
firms were most likely caused by differences in economic and technological expertise and changes
across time and competing countries.
A qualitative and case study approach has been applied by Ooghe (1998) to investigate into the
financial management practices of Chinese firms in the Shanghai region. The financial manages of
16 diverse firms from different industrial sectors in Shanghai were interviewed on the basis of an
extensive questionnaire. The results of interview revealed that many firms do not have formal
working capital policies especially for accounts receivables and payables. Firms faced big problem
in the collection of receivables due to the lack of clear credit and collection policy. The amounts in
accounts payable were sometimes very high and proper payment systems were not followed.
The issue of aggressive and conservative working capital policies on empirical basis has been
discussed by Weinraub and Visscher (1998) who analyzed working capital policies of 126 industrial
firms from 10 diverse industrial groups using quarterly data for a period of 1984 to 1993. The
primary objective was to observe the differences in working capital policies as well as the long-term
stability of working capital policies over time. Current assets to total assets ratio and current
liabilities to total asset ratio were used as working capital investment and financing policies.
Analysis of Variance (ANOVA) and Tukey’s HSD tests clearly indicate significant differences in
working capital policies across various industries. Rank order correlation analysis confirmed the
stability of these polices over ten year period of time. Furthermore, the negative relationship
between industry working capital investment and financing policies indicated that when relatively
aggressive working capital investment policies were followed, they were balanced by relatively
conservative working capital financing policies.
25
Khoury et al. (1999) extended the work of Smith and Sell (1980), Belt and Smith (1991) by
comparing the working capital practices of Canadian firms with United States and Australian firms.
The 45-Items questionnaire was mailed to 350 firms randomly selected from 10 industries within
the BOSS database obtained from the Ministry of Industry, Science and Technology. Only 7% of
the respondents had formal working capital policy, which is much smaller than the previous studies.
The 28.5% of Canadian firms have cautious working capital policy while only 10.2% of the
respondents have an aggressive working capital policy. On average, the results were not much
different from the previous survey conducted in U.S and Australia.
Anand & Gupta (2002) established then quantitative benchmarks for working capital performance
evaluation of Corporate India. These included days operating cycle, days working capital and cash
conversion efficiency. The basic thrust of the paper was to estimate these three benchmarks for
firms and industries as well as to observe the differences among industries and over the period of
time. The 427 companies of S&P 500 companies of corporate India were analyzed for the period of
1999-2001. The results indicated that the working capital performance of corporate India varies
across industries based on the above-mentioned measures of working capital. Moreover, the
measured values of working capital changed over time and were not stable across the period of
study.
To provide empirical evidence from an under-developed market, Sathyamorthi (2002) analyzed the
working capital practices of selected co-operatives in Botswana for a period of 1994 to 1997. The
study looked at how current assets are financed and to establish a trend over the period of time. The
financial ratios were used to obtain a correct picture of the working capital movements in cooperative societies. Measures of central tendency and dispersion were applied to the working capital
ratios. The results indicated that, on average, the selected co-operatives in Botswana adopted an
aggressive approach during the study period in terms of working capital asset management.
Moreover, the co-operative societies adopted a conservative policy to finance the current assets in
all the four years of study.
Raman & Kim (2002) used a case study approach to quantify the relationship between inventory
carrying costs and a firm’s target stock out costs. The study was motivated by the inventory
management and stock out problems at North Co., a school uniform producer in U.S. Using the
linear programming approach, the authors suggested that firms with high working capital costs
should target substantially higher stock out costs and higher capacity levels in that particular
industry
26
Hall (2002) introduced a “Total” approach to working capital management and argued that this
“Total” approach covered all the company’s activities relating to vendor, customer and product.
Accounts receivables management was termed as Revenue Management, which included searching
for customers to collecting cash form them for credit sales. Inventory management, also named as
Supply Chain Management, started from the forecasting of the customer claimed and ended with
the fulfillment of the forecasted demand. The third working capital component, account payables
was proxied with expenditure management, included the whole process of purchasing from vendors
to pay the cash. The study argued that this integrated or “Total” approach to working capital
management would reduce inefficiencies in the business processes as well as improve firm
performance.
Howorth & Westhead (2003) examined the eleven working capital management routines of 343
small companies in UK through a mailed questionnaire. Principal component analysis and
discriminate analysis reduced the number of working capital management routines form 11 to 3 and
company types to 4 categories respectively. The multinomial logistic regression has been used to
identify various independent variables that discriminate between the companies, which follow or
did not follow working capital management routines. The results indicated that companies focusing
on cash management were larger, younger in age, with fewer cash sales, more seasonality and more
external finance. Firms focusing on stock management routines were smaller, younger with less
external finance and longer production cycles. Credit management routines were focused by firms,
which had lower profitability, more credit purchases & sales, and high growth firms. On the
contrary, firms focusing least on any of working capital management routines were low growth,
high profitable, having less external finance, fewer credit purchases & sales, having shorter
production cycles and less sophisticated financial skills in general results.
The model of Soenen (1993) has been used by Deelof (2003) to investigate the relationship between
the working capital management and corporate profitability for a sample of annual data of 1009
large Belgian non-financial firms for the period of 1992-1996. The cash conversion cycle has been
used as a comprehensive measure of working capital management, whereas gross operating income
has been used as a measure of corporate profitability. In addition, size of the firm, sales growth, the
financial debt and ratio of fixed financial assets to total assets were introduced as control variables.
The results of Pearson correlation and ordinary least square regression confirmed the findings of
Shin & Soenen (1998) and found a significant negative relationship between cash conversion cycle
27
and corporate profitability. It is suggested that the managers can create value for shareholders by
maintaining the cash conversion cycle and its components to an optimal level.
Eljelly (2004) empirically analyzed 29 Saudi joint stock companies for a period of 1996-2000 to
examine the relationship between liquidity and profitability. The study used net operating income as
dependent profitability measure and cash gap (cash conversion cycle) and current ratio as
independent liquidity measures whereas the size of firms as measured by sales and industry effect
has been controlled for detailed analysis. A strong negative relationship has been reported between
the liquidity measures and net operating income by Pearson correlation and pooled regression
analysis. The study further confirmed that cash gap produced more significant results at industry
level than the current ratio. Moreover, the size is also affecting the profitability of firms and these
results are stable over the period of study.
The efficiency of working capital practices in Indian cement industry has been examined by Ghosh
and Maji (2004) using some alternate measures of working capital management. Three measures;
performance index of working capital, utilization index of working capital and efficiency index of
working capital were used to measure the overall efficiency of Indian cement manufacturing firms.
The data about 20 large cement industries was collected for 10 years from 1992-2001. The results
indicated that Indian cement industry did not perform remarkably well during the study period in
terms of working capital management. Industry average for efficiency index of working capital was
greater than 1 for only 6 years out of 10 years of study. However, some of the sample firms
improved their efficiency index during the study period but a high degree of inconsistency is found
into working capital management practices.
Another study by Teruel and Solano (2005) provided empirical evidence of impact of working
capital management on profitability of small and medium sized Spanish firms. Cash conversion
cycle along with its components has been taken as independent variables whereas return on assets
has been used as dependent measure for profitability. The data set was consisted of 8,872 SMEs
covering the period of 1996-2002 was obtained from ABADEUS database of Spain. A strong
negative relationship between return on assets and cash conversion cycle along with its components
i.e. days accounts receivables, days inventory and days accounts payable was indicated by
correlation analysis. The multivariate regression analysis confirmed this negative relationship that
shortening the cash conversing cycle, firms can generate more profits for shareholders. The
regression results were found significant for negative relation between return on assets and
inventory turnover as well as days accounts receivables. However, impact of delaying payment to
28
suppliers on return on assets remained inconclusive because it was not significant at 5% level of
significance.
In order to confirm the findings of earlier studies, Filbeck and Krueger (2005) analyzed the working
capital efficiency of firms as per the ranking made by CFO magazine in the annual working capital
surveys for 1996-2000. The study examined the differences in working capital efficiency across
industries as well as stability of working capital measures across time. The differences in working
capital efficiency across industries were evaluated by using Analysis of Variance (ANOVA) test.
The results of ANOVA test suggested that differences do exist among industries in respect of
working capital measures i.e. days of working capital, inventory turnover, days payables and days
sales outstanding. Furthermore, Kendall’s concordance coefficient indicated that the working
capital measure very across time. However, these changes were consistent across industries to
pressure industry ordering across time.
On the other hand, Enyi (2005) examined the relationship between the operational size of the firm
and the adequacy of the working capital requirements in Nigeria. Relative solvency ratio has been
used to measure the level of working capital that can be considered adequate for the firm size and
operational level. A relative solvency ratio greater than one was considered to be adequate for
working capital level requirements relative to the operational size of the firms. The data has been
collected from the annual published repots of 25 companies listed in Nigeria Stock Exchange
together with the interviews of selected officials of the firms. T-test has been applied to compare the
relative solvency ratio and return on capital employed as the performance measure of firms having
relative solvency ratio greater than one with those that were less than one. The results indicated that
firms having relative solvency ratio greater than 1 i.e. adequate working capital relative to its
operational size perform better than firms who have inadequate working capital.
Medeiros (2005) empirically tested the Michel Fleuriet’s advanced working capital management
model by taking a sample of 80 Brazilian firms listed at Sao Paulo stock exchange for a period of
1995-2002. The Fleuriet’s model was presented in Brazil in 1980 for advanced analysis of working
capital management. The basic thrust of the model was that it divided the current assets and
liabilities into current financial and current operating assets and liabilities respectively. Fleuriet
claimed that current financial assets and current financial liabilities were erratic and not related to
firms operations; however, this model was not tested on empirical grounds. This study tried to
validate model by investigating into the relationship between current financial assets and liabilities
as well as current asset and liabilities as whole with the net operating revenues of firms. The
Pearson correlation and cross-sectional regression analysis confirmed that there is strong positive
29
relationship between firms operations and current financial assets and liabilities as well as current
operating assets and liabilities. Moreover, panel data regression results further confirmed the
expectations and, hence, rejected the Fleuriet’s model of dynamic working capital management,
which was considered to be one of the advanced models of financial management in Brazil since
1980.
From another angle, Chiou et al. (2006) have analyzed the determinants of working capital
management by using net liquid balance and working capital requirements of a firm as measures of
working capital management of a firm. The paper explored that how working capital management
of a firm is influenced by the different variables like business indicators, industry effect, operating
cash flows, growth opportunity for a firm, firm performance and size of firm. Using Taiwan’s TEJ
database, 19180 quarterly observations have been collected for a period of 1996 to 2004. Net liquid
balance was being affected by economic recession, operating cash flow and growth opportunities of
firm positively and significantly except growth which was not found to be statistically significant.
Whereas, firm cycle, leverage, age of the firm, return on assets and firm size were having negative
and statistically significant relationship with the net liquid balance. Coming towards working
capital requirements, economic recession, age and firm size were relating to working capital
requirements positively and significantly, whereas, cycle, leverage, operating cash flow, growth and
return on assets were having significant negative relationship with firm’s requirements of working
capital.
The result showed that firms operate on a loose working capital policy in the times of economic
recession because it is hard to acquire external capital during recession so a relatively higher level
of liquid assets is maintained. The results of leverage are consistent of pecking order theory that
firm is supposed to maintain a higher debt ration where its working capital is depleted. Companies
may get external borrowings instead of issuing securities with relatively low cost. The large
companies are found to produce more net liquid balance from their operations. Moreover,
companies with better performance and having higher return on assets tend to have a more
conservative approach towards managing their working capital having more capital for
contingencies. The study has also proved that when operating cash flow increases in a firm, the
managers imply more efficient working capital management policy. The authors are in a view that
the study has provided consistent results of leverage and operating cash flow for both net liquid
balance and working capital requirements, however, variables like business indicator, industry
effect, growth opportunities, performance of firm, and size of firm were unable to produce
consistent conclusions for net liquid balance and working capital requirements of firms. The
30
similar study was also conducted by Nazir and Afza (2008) on a sample of Pakistani market and
found somehow analogous results.
More recently, Lazaridis and Tryfonidis (2006) investigated the relationship between working
capital management and corporate profitability using quarterly date of 131 firms listed at Athens
Stock Exchange of Greece. Cash conversion cycle has been used as a measure of working capital
management whereas gross profit has been taken as profitability measure. The size of the firms as
measured by natural log of sales, financial debt of firm and fixed financial assets to total assets ratio
were used as control variables. Person correlation analysis showed a negative relationship between
gross profit and cash conversion cycle as well as of the number of days accounts receivables and
inventory while a positive relationship between gross profit and number of days accounts payables.
In order to validate the robustness of correlation results, four regressions were run to examine the
individual impact of cash conversion cycle and its components on gross profit. The regression
results confirmed previous findings that cash conversion cycle, number of days accounts
receivables and inventory were negatively while the number of days accounts payables were
positively related to gross profit. All results were statistically significant at 1% level of significance
indicating that managers can create profits by keeping current assets and current liabilities to an
optimal level.
In Pakistan, a little work has been found in finance literature, specifically with reference to shortterm financial management and working capital. However, researchers have discussed the other
areas of finance like capital structure, corporate governance, market efficiency, diversification
strategy, and investment analysis. In this regard, Mir and Nishat (2004) compared the Pakistani
firms’ performance under different corporate governance structures using parameters like
ownership structure and identity of owners, board characteristics, financial policy, and control
variables like leverages, size and firm risk beta. They measured firm performance through return on
asset, Tobin’s q and stock return. Their results showed that firm performance is positively
influenced by corporate governance structure variables, while it is adversely affected in case where
CEO also heads the Board of Directors. On the same lines, Ghani and Ashraf (2005) examined
different business groups and their impact on corporate governance in Pakistan for 582 nonfinancial firms for the period of 1998 to 2002. The results indicated that group firms have higher
liquidity, short-term debt repay capacity and lower financial leverage as well as higher sales growth
than the non-group firms in each of the study period. Moreover, the returns on assets were higher
for group firms but lower Tobin’s q than the non-group firms.
31
In addition, Afza et al. (2006) empirically examined the relationship between the diversification
strategy and the performance of sixty-three non-financial firms of Karachi Stock Exchange for a
period of three years from 2001-2003. The regression analysis indicated that the best performing
firms, if diversify, may reduce their earnings in emerging economy accompanied by increased risk.
Therefore, by following the strategy of diversification the market share of firm may increase
whereas the profitability may decrease due to higher competition in market. Moreover, Ahmad and
Zaman (2001) analyzed the relationship between the uncertainty and returns of firms listed at
Karachi Stock Exchange (KSE). Hussain (2001) examined the efficiency of Pakistani equity
market, while Irfan and Nishat (2002) applied a case study approach to the key fundamental factors
and long run price movements in Karachi Stock Exchange (KSE).
However, a related domestic study by Rehman (2006) analyzed the impact of working capital
management on 94 Pakistani firms listed in Islamabad stock exchange for a period of 1999-2004.
The cash conversion cycle and its components i.e. day accounts receivable, inventory and payable
along with current ratio has been used as independent variables while net operating income has
been used as profitability measure. The results of correlation and pooled & ordinary least square
regression analysis confirmed the negative relationship between working capital measures and net
operating income of firms as per the previous studies. Reduction in cash conversion cycle and its
components leads to improved firm performance in Pakistan.
Finally, Afza and Nazir (2007a) investigated the relationship between the aggressive/conservative
working capital policies for seventeen industrial groups and a large sample of 263 public limited
companies listed at Karachi Stock Exchange for a period of 1998-2003. Using ANOVA and Least
Significant Difference (LSD) test, the study found significant differences among their working
capital investment and financing policies across different industries. Moreover, rank order
correlation confirmed that these significant differences were remarkably stable over the period of
study. The aggressive investment working capital policies were accompanied by aggressive
working capital financing policies. Finally, ordinary least regression analysis found a negative
relationship between the profitability measures of firms and degree of aggressiveness of working
capital investment and financing policies. These results were further confirmed by Afza and Nazir
(2007b) on a longer period of time (i.e. 1998-2005) and using market measures of return. Moreover,
the later study also took into consideration the impact of aggressiveness of working capital policies
on the risk of firm. In conformity with Carpenter and Johnson (1983), the study found no significant
relationship between the aggressiveness\conservativeness of working capital policies of firms and
their operating and financial risk.
32
Keeping in view the miniature amount of finance literature, particularly in working capital, the
present study investigates the relationship of the aggressive and conservative working capital asset
management and financing polices and impact on profitability. It further examines whether
significant differences exist among the working capital practices of the firms across different
industries and whether these aggressive or conservative working capital policies are relatively stable
over the period of time. Moreover, the study validates the relationship between working capital
asset management and financing policies of a particular firm and examines how a working capital
asset management policy corresponds to working capital financing policy. Finally, the impact of
aggressive and conservative working capital asset management and financing policies on the risk
and profitability of the company has also been explored. In the light of above discussion and
literature review, the testable hypotheses formed are:
H01 = There is no difference among the working capital investment policies of firms
across different industries.
Ha1 = There is a difference among the working capital investment policies of firms
across different industries.
H02 = There is no difference among the working capital financing policies of firms
across different industries.
Ha2 = There is a difference among the working capital financing policies of firms
across different industries.
H03 = The working capital policies are not relatively stable over the longer period of
time.
Ha3 = The working capital policies are relatively stable over the longer period of time.
H04 = An aggressive investment working capital policy is not accompanied by a
conservative financing policy.
Ha4 = An aggressive investment working capital policy is accompanied by a
conservative financing policy.
H05 = A conservative investment working capital policy is not accompanied by an
aggressive financing policy.
Ha5 = A conservative investment working capital policy is accompanied by an
aggressive financing policy.
33
H06 = An aggressive working capital policy is inversely related to firms’ profitability.
Ha6 = An aggressive working capital policy is directly related to firms’ profitability.
H07 = An aggressive working capital policy is directly related to firms’ risk.
Ha7 = An aggressive working capital policy is inversely related to firms’ risk.
The above mentioned hypotheses have been validated using various quantitative data analysis
techniques like ANOVA, rank order correlations, regression analysis. ANOVA has been further
supported by HSD, LSD and Bonferroni post hoc tests whereas, the regression analysis done is
organized in year-wise regression, using mean values and using panel data analysis. The details
discussions of these statistical techniques are presented in the following section of methodology.
34
Chapter 3
RESEARCH METHODOLOGY
3.1 Variables of the Study
The study used aggressive investment policy and conservative investment policy as measuring
variables of working capital management as used by Weinraub and Visscher (1998) who analyzed
working capital policies of 126 industrial firms in US market. Aggressive Investment Policy (AIP)
results in minimal level of investment in current assets versus fixed assets. In contrast, a
conservative investment policy places a greater proportion of capital in liquid assets with the
opportunity cost of lesser profitability. As the level of current assets is increased in proportion to the
total assets of the firm, the management is being more conservative in managing the current assets
of the firm. In order to measure the degree of aggressiveness of Working Capital Assets
Management policy, following ratio has been used:
AIP
=
Total Current Assets (TCA)
Total Assets (TA)
: Where a lower ratio means a relatively aggressive policy.
On the other hand, Aggressive Financing Policy (AFP) utilizes higher levels of current liabilities
and less long-term debt. In contrast, a conservative financing policy uses more long-term debt and
capital and less current liabilities. The firms are more aggressive in terms of current liabilities
management if they are concentrating on the use of more current liabilities which put their liquidity
on risk. The degree of aggressiveness of a financing policy adopted by a firm will be measured by
Working Capital Financing Policy and following ratio has been used:
AFP
=
Total Current Liabilities (TCL)
Total Assets (TA)
: Where a higher ratio means a relatively aggressive policy.
The impact of working capital policies on the profitability has been analyzed through accounting
measures of profitability as well as market measures of profitability. In the accounting measures,
35
frequently used Return on Assets (ROA) and Return on Equity (ROE) are applied. These accounting
variables of return are calculated as:
Return on Assets (ROA) =
Return on Equity (ROE) =
Net Earnings after Taxes (NEAT)
Book Value of Assets (BVA)
Net Earnings after Taxes (NEAT)
Book Value of Equity (BVE)
In the market measures of profitability, Market Return (MKRT) has been taken as the third
performance variable in the market measures of profitability. Market Return (MKRT) is calculated
by the following equation:
Market Return (MKRT) =
(Pt - Pt-1 ) + Divt
Pt-1
Where:
Pt = Price of the stock of a firm at time period t
Pt-1 = Price of the stock of a firm at time period t-1
Divt = Dividend paid by a firm at time period t
Furthermore, Tobin’s q is used as the fourth market measure of return. Tobin's q compares the value
of a company given by financial markets with the value of a company's assets. A low q (between 0
and 1) means that the cost to replace a firm's assets is greater than the value of its stock.
This implies that the stock is undervalued. Conversely, a high q (greater than 1) implies that
a firm's stock is more expensive than the replacement cost of its assets, which implies that the stock
is overvalued. It is calculated as:
Tobin’s q
=
Market Value of Firm (MVF)
Book Value of Assets (BVA)
Where:
: Market Value of Firm (MVF) is the sum of Book Value of long plus short term debt and
market value of equity. Market value of equity is calculated by multiplying the number of
shares outstanding with the current market price of the stock in a particular year.
36
The last variable in the study is the risk faced by the firms when opting for the aggressive working
capital management policies. The risk is measured by the variability in the returns of firms.
Standard Deviation (SD) of the all four profitability measures is used as the risk of a particular firm.
3.2 Control Variables
In working capital literature, various studies have used the control variables along with the main
variables of working capital in order to have a pertinent analysis of working capital management on
the profitability of firms (Lamberson 1995; Smith & Begemann 1997; Deelof 2003; Eljelly 2004;
Teruel and Solano 2005; Lazaridis and Tryfonidis 2006). On the same lines, along with working
capital variables, the present study has taken into consideration some control variables relating to
firms like the size of the firm, the growth in its sales, and its financial leverage. The size of the firm
(SIZE) has been measured as the logarithm of its total assets as the original value of total assets may
perturb the analysis. The growth of firm (GROWTH) is measured by variation in its annual sales
value with reference to previous year’s sales [SGROW as (Salest – Salest-1)/Salest-1]. Moreover, the
financial leverage (LVRG) has been taken as the debt to equity ratio of each firm for the whole of
sample period. Some researchers like Dellof (2003), in his study of large Belgian firms, also
considered the ratio of fixed financial assets to total assets as a control variable; however, this
variable can not be included in current study because of unavailability of appropriate data as most
of firms don’t disclose the full information in the financial statements. Finally, since good economic
conditions tend to be reflected in a firm’s profitability (Lamberson 1995), this phenomenon has
been controlled for the evolution of the economic cycle using the variable GDPGR, which measures
the annual GDP growth in Pakistan for each of the study year of 1998-2005.
3.3 Statistical Analysis
One-way ANOVA test has been used to compare the means of working capital assets management
and financing polices of various industrial sector and to find out the significant difference among
the sectoral working capital policies. The result are further validated by performing Least
Significant Difference (LSD) and Bonferroni test on the mean values of TCA/TA ratio and TCL/TA
ratio to measure the differences in the aggressive and conservative working capital assets
management and financing policies across the industrial groups. Furthermore, Rank Order
Correlation has been used to test the relative stability of the policies over the years of study.
37
Next is to examine that how aggressive working capital assets management policy corresponded to
aggressive financing policy. This relationship has been tested on yearly basis. For the first year, the
industrial sectors are ranked from low CA/TA ratios to high ratios, corresponding to ascending
order of relatively aggressive policies. The same procedure has been done for financing policies of
firms. Then, Rank order correlations between the two policies are computed for year one. This
procedure has been repeated for each of the remaining seven years.
The impact of aggressive and conservative working capital policies on the profitability and risk of
the firm has been evaluated through applying the regression analysis. The performance variables
(ROA, ROE, MRKT, Tobin’s q) as well as the TCA/TA and TCL/TA along with the control
variables has been regressed using cross sectional regressions. The following regression equations
are run to estimate the impact of working capital policies on the accounting as well as market
measures of profitability. The proposed regression models are as follows:
ROA it = α + β1 (TCA/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) + ε
………… (1)
ROE it = α + β1 (TCA/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) + ε
………… (2)
MRKT it = α + β1 (TCA/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) + ε
………… (3)
Tobin’s q it = α + β1 (TCA/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) +
ε ………… (4)
And
ROA it = α + β1 (TCL/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) + ε
………… (5)
ROE it = α + β1 (TCL/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) + ε
………… (6)
38
MRKT it = α + β1 (TCL/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) + ε
………… (7)
Tobin’s q it = α + β1 (TCL/TA it) + β2 (SIZE it) + β3 (GROWTH it) + β4 (LVRGit) + β5 (GDPGR it) +
ε ………… (8)
Where:
ROA it
=
Return on Assets of Firm i for time period t
ROE it
=
Return on Equity of Firm i for time period t
MRKT it
=
Market Rate of Return of Firm i for time period t
Tobin’s q i =
Value of q of Firm i for time period t
TCA/TA it
=
Total Current Assets to Total Assets Ratio of Firm i for time period t
TCL/TA it
=
Total Current Liabilities to Total Assets Ratio of Firm i for time period t
α
=
intercept
ε
=
error term of the model
The impact of the working capital assets management and financing polices on the relative risk has
been measured by applying regression models for the risk of the company and its working capital
management policies over the period of 1998-2005. The regression equations are:
SDROAi = α + β1 (TCA/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR i) + ε
………… (9)
SDROEi = α + β1 (TCA/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR i) + ε
………… (10)
SDMARKTi = α + β1 (TCA/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5
(GDPGR i) + ε ………… (11)
SDqi = α + β1 (TCA/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR i) + ε
………… (12)
SDSalesi = α + β1 (TCA/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR i) + ε
………… (13)
And
39
SDROAi = α + β1 (TCL/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR i) + ε
………… (14)
SDROEi = α + β1 (TCL/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR t) + ε
………… (15)
SDMARKTi = α + β1 (TCL/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5
(GDPGR i) + ε ………… (16)
SDqi = α + β1 (TCL/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5 (GDPGR i)
+ ε ………… (17)
SDSalesi = α + β1 (TCL/TA i) + β2 (SIZE i) + β3 (GROWTH i) + β4 (LVRGi) + β5
(GDPGR i) + ε ………… (18)
Where:
SDi
=
Standard Deviation representing risk of Firm i
3.4 Sample & Data
The total population of the study is all non-financial firms listed at Karachi Stock Exchange.
Karachi Stock Exchange (KSE) has divided the non-financial firms into various industrial sectors
based on their nature of business. The number of firms in these industrial sectors varies from 6 to 37
with the exception of Textile Composite and Textile Spinning sectors having 62 and 157 firms
respectively. In order to be included in the population, a firm must be in business for the whole
study period. Neither of the firms should be de-listed by the Karachi Stock Exchange (KSE) nor
should it be merged with any other firm during the whole window period. The merged and delisting from the Karachi Stock Exchange, due to any reason/restriction imposed by the regulators,
make the firm ineligible to be included in the study. New incumbents in the market during the study
period have also not included in the population. Furthermore, firms must have complete data for the
period of 1998-2005. Firms with negative equity during the study period have also been removed
for the population of study.
40
As a first step, 438 non-financial firms were selected whose financial data was available for the
study period i.e. 1998-2005. Furthermore, firms with missing data, negative equity and negative
profitability for study period were deleted from the sample leaving us with the final population of
204 non-financial firms from 17 industrial sectors. The whole population has been taken as the
sample for analysis of working capital policies. The list of sample companies along with respective
industrials sectors has been included in the annexure number 1 at the end of the report.
The study has used annual financial data of 204 non-financial firms for the period of 1998-2005.
For the data collection purpose, various sources have been utilized. The book value based required
financial data of these firms was obtained from the companies’ annual reports and publications of
State Bank of Pakistan. The data regarding annual average market prices has been collected from
the daily quotations of Karachi Stock Exchange (KSE) and used for analysis by the above
mentioned statistical techniques and results are discussed in the coming chapter.
41
Chapter 4
ANALYSIS and DISCUSSIONS
4.1 Descriptive Analysis
Table 4.1 presents the descriptive analysis of 204 non-financial public limited firms of Karachi
Stock Exchange (KSE) from 1998 to 2003. The study has used nine variables for the analysis
purpose including five independent variables and four dependant performance measures. The
independent variables are investment policy and financing policy of the firms as a measure of
working capital management of the sampled firms. Other three independent control variables used
are size as measured by the total assets of the firm, growth of firms as relative change in sales as
compared to previous year and leverage of the firms. All the variables are averaged for each firm
for all eight years and presented in sixteen sectors as categorized by the Karachi Stock Exchange
(KSE). The standard deviation is the variation of these ratios for each year and an average value has
been calculated for each industry by the same method.
Panel A of Table 4.1 presents the statistics for working capital management of firms and size of
firm along with the number of firms in each sector of Pakistani market. The number of firms varies
from 3 to 40 firms in each industry accumulating the total sample of the study to 204 non-financial
public firms. The smallest sector of present study is leather and tanneries sector having only 3 firms
whereas, on the other side, textile spinning sector has 40 firms. On average, we do have a sufficient
number of companies in each of the sector. The mean values of TCA/TA ranges from 0.0682 to
0.9265 of cement sector and automobiles sector respectively. Approximately all the sectors are
having average of investment policy near to 0.50 except cement and leather sector. Cement sector is
found to be more aggressive in managing the current assets where its average TCA/TA ratio is
0.2758. On the other hand, leather and tanneries sector is much conservative to its currents assets
management having TCA/TA ratio of 0.8322. The variation in the TCA/TA is less than 0.1 for all
the industrial sectors. Remaining sectors are in the middle of those extreme values and are neither
too much conservative nor aggressive while managing current assets.
42
TABLE 4.1 (Panel A): Descriptive Statistics for Study Variables
Industries
Investment Policy
N
Financing Policy
Size (In Million Rupees)
Mean
SD
Min
Max
Mean
SD
Min
Max
Mean
SD
Min
Max
Automobiles and Allied
15
0.7059
0.1346
0.3347
0.9265
0.4972
0.0845
0.3762
0.6547
2,314
2,495
150
8,505
Cables and Electric
4
0.7725
0.1657
0.5265
0.8808
0.5778
0.1020
0.4478
0.6957
2,592
2,197
706
4,886
Cement
12
0.2758
0.1884
0.0824
0.7821
0.2624
0.0903
0.1471
0.4235
3,894
3,118
822
10,403
Chemicals and Pharma.
22
0.6598
0.1698
0.2767
0.8869
0.4207
0.1244
0.2202
0.6159
3,804
6,424
103
22,611
Engineering and Allied
7
0.5924
0.1949
0.3032
0.8762
0.4166
0.1511
0.2142
0.5932
1,096
935
86
2,375
Food and Allied
15
0.6030
0.1327
0.3995
0.8304
0.5056
0.1571
0.2769
0.8025
1,237
1,809
161
6,541
Fuel and Energy
17
0.5412
0.2092
0.2364
0.9159
0.4479
0.2142
0.1444
0.8415
14,089
18,055
106
58,696
Glass and Ceramics
4
0.5089
0.1054
0.4050
0.6258
0.3094
0.1214
0.1855
0.4751
582
53
512
630
Leather and Tanneries
3
0.8322
0.0548
0.7956
0.8953
0.6871
0.1081
0.5991
0.8077
1,175
755
357
1,845
Paper and Board
7
0.5977
0.2057
0.3476
0.8667
0.3139
0.1056
0.1457
0.5033
1,680
2,344
69
6,832
Sugar
18
0.4218
0.0974
0.2735
0.5789
0.4306
0.1052
0.1937
0.6152
988
427
431
1,708
Synthetic & Rayon
9
0.4409
0.1862
0.1506
0.7466
0.3655
0.1549
0.1720
0.6454
4,280
5,604
130
16,221
Tex. Composite
21
0.5036
0.1365
0.2295
0.7217
0.4663
0.1037
0.2718
0.7328
3,051
3,165
50
14,431
Tex.Spinning
40
0.4963
0.1302
0.0682
0.7857
0.4871
0.1039
0.2452
0.7402
1,036
807
182
3,676
Tex. Weaving
5
0.5225
0.1024
0.3793
0.6541
0.5260
0.1157
0.3300
0.6377
980
434
580
1,636
5
204
0.4235
0.1008
0.3211
0.5329
0.3708
0.0935
0.2574
0.5125
38,618
57,512
328
134,434
0.5359
0.1858
0.0682
0.9265
0.4440
0.1431
0.1444
0.8415
4,013
11,895
50
134,434
Transport & Comm.
Total
43
TABLE 4.1 (Panel B): Descriptive Statistics for Study Variables
Industries
N
Growth of Sales (In %age)
Leverage
Return on Assets (In %age)
Mean
SD
Min
Max
Mean
SD
Min
Max
Mean
SD
Min
Max
1.42
Automobiles and Allied
15
22.38
12.04
43.44
1.4159
0.4681
0.7436
2.1955
8.62
5.47
0.90
22.12
Cables and Electric
4
110.91
374.11 (231.32) 644.22
17.0808
29.9640
1.3991
62.0206
2.84
4.17
(1.87)
8.11
Cement
12
8.87
11.36
(2.50)
38.14
1.8699
1.4887
0.5196
4.3879
1.58
5.25
(8.51)
12.22
Chemicals and Pharma.
22
9.85
9.97
(28.90)
18.35
1.3213
0.9377
0.2979
4.2782
8.38
7.71
(15.20) 17.34
Engineering and Allied
7
37.84
37.63
12.30
117.19
4.5093
7.9873
0.4035
22.5222
1.63
12.59 (21.14) 18.01
Food and Allied
15
15.05
13.32
(0.36)
50.46
4.1299
6.0884
0.6364
24.2960
8.12
8.62
(7.40)
23.00
Fuel and Energy
17
11.19
10.48
(6.90)
28.04
2.5709
2.9851
0.3853
12.7308
6.60
5.52
(2.09)
22.17
Glass and Ceramics
4
10.87
27.35
(25.94)
40.17
7.9594
14.5222
0.2946
29.7368
8.73
12.92
(5.52)
25.53
Leather and Tanneries
3
6.39
2.98
2.96
8.33
3.2518
1.6846
2.0164
5.1707
1.64
1.87
(0.52)
2.73
Paper and Board
7
7.23
5.95
0.44
16.03
1.0653
1.1901
0.1885
3.6949
10.28
8.32
0.18
24.90
Sugar
18
11.35
8.47
(4.40)
25.07
1.8143
1.2029
0.3513
4.2629
3.63
4.70
(3.93)
12.01
Synthetic & Rayon
9
12.84
15.14
(17.18)
32.74
1.8753
1.1267
0.3898
3.9772
4.29
2.42
0.57
7.27
Tex. Composite
21
21.58
25.98
4.28
123.21
2.1432
1.1294
0.7188
5.5228
5.33
3.89
0.03
16.03
Tex.Spinning
40
13.54
22.90
(0.06)
145.31
2.5625
1.3073
1.1238
5.9700
4.66
3.80
(5.76)
15.56
Tex. Weaving
5
12.26
5.01
5.96
18.48
3.0042
1.1744
1.2324
4.5103
3.55
3.32
0.51
8.78
Transport & Comm.
5
13.68
18.41
(8.95)
41.09
2.4116
3.0187
0.6076
7.7490
6.74
5.98
(1.44)
15.37
204
16.38
50.77
(231.32) 644.22
2.7032
5.3055
0.1885
62.0206
5.66
6.29
(21.14) 25.53
Total
44
TABLE 4.1 (Panel C): Descriptive Statistics for Study Variables
Industries
N
Return on Equity (In %age)
Tobin's Q
Market Returns (In %age)
Mean
SD
Min
Max
Mean
SD
Min
Max
Mean
SD
Min
Max
17.63
10.64
0.51
39.20
47.63
21.68
14.84
87.48
1.0375
0.1532
0.7875
1.3593
19.55
40.72
28.38
(0.42)
62.63
1.0860
0.2211
0.7790
1.2766
Automobiles and Allied
15
Cables and Electric
4
Cement
12
2.81
7.59
(4.12)
19.67
46.29
16.27
23.40
71.47
0.9377
0.1919
0.6420
1.2862
Chemicals and Pharma.
22
14.31
15.04
(33.21)
33.13
33.65
12.79
17.35
69.85
1.5480
1.3700
0.6915
7.4358
Engineering and Allied
7
(9.37)
45.10
(91.88)
30.75
55.21
29.63
11.89
107.61
1.0514
0.1977
0.8479
1.2939
Food and Allied
15
10.81
63.56
(169.63)
107.50
33.38
21.26
(1.25)
80.01
2.2225
2.8081
0.7064
11.9220
Fuel and Energy
17
16.31
12.35
(9.96)
34.19
42.71
20.80
17.90
93.22
1.2108
0.4035
0.5790
2.1435
Glass and Ceramics
4
(22.02)
69.14
(121.43)
32.48
50.81
17.03
31.77
70.86
0.8752
0.3264
0.5623
1.1882
Leather and Tanneries
3
2.87
9.02
(7.42)
9.42
18.84
10.58
7.12
27.71
0.9424
0.0375
0.9102
0.9835
Paper and Board
7
15.20
11.07
0.12
29.74
40.51
8.52
24.73
50.36
1.0053
0.2362
0.5775
1.1997
Sugar
18
3.53
12.50
(29.00)
19.47
38.62
21.25
7.97
94.74
0.8315
0.1651
0.5935
1.3215
Synthetic & Rayon
9
8.93
7.75
(6.38)
19.80
38.10
20.44
5.19
70.07
0.9534
0.1506
0.7310
1.1200
Tex. Composite
21
13.59
9.16
(5.19)
32.28
49.77
16.48
14.45
73.08
0.9521
0.1912
0.7377
1.5569
Tex.Spinning
40
12.41
12.92
(19.33)
53.48
43.79
23.18
5.98
102.88
0.9298
0.2034
0.6478
1.8373
Tex. Weaving
5
9.77
8.91
(0.52)
18.57
36.91
13.81
25.86
52.45
0.9170
0.0787
0.8266
1.0242
Transport & Comm.
5
8.43
13.09
(10.34)
23.94
45.78
31.42
15.43
98.80
1.1080
0.2813
0.8022
1.5278
204
4.71
81.81
(1,107.02) 107.50
42.05
20.58
(1.25)
107.61
1.1310
0.9543
0.5623
11.9220
Total
(267.40) 559.78 (1,107.02)
45
The TCL/CA, on average, is near about 0.40 except the Cables & Electrical Sector and Leather &
Tanneries having TCL/TA ratios of 0.5778 and 0.6871 respectively. The most aggressive sector in
current liabilities management is leather sector having approximately 70% of its financing generated
from short term sources of funds. Whereas, the cement sector in very much conservative in short term
liabilities management and generate only 26% funds from short term payables. However, the variation
in financing policies is relatively lesser over the eight years of study as compared to investment policies
with almost half of the industries having standard deviation less than 0.1.
Size of the firms is given in the third main column of Panel A of Table 4.1. Size is also averaged for
eight years of study i.e. 1998-2005. The smallest sector is glass and ceramics sector which has
maximum of 630 millions rupees of total assets with an average size of firms in the sector of 582
million rupees. Other small sectors includes textile weaving and sugar sectors which have, on average,
total assets worth of less than 1000 million rupees. The larger sectors include fuel and energy and
transportation sectors. In fuel and energy, we do have some large refineries whereas Pakistan
International Airlines and Pakistan Telecommunication Company make the transportation sectors the
larger with more total assets.
Panel B of Table 4.1 represents descriptive statistics for growth of firms, leverage and return on assets
for 204 sampled firms. Cables and Electric sector is one having maximum growth opportunities with
more than 100% growth in sales. Whereas leather, paper & board, cement and chemicals &
pharmaceutical sectors are sectors with the minimum growth rates of 6.39%, 7.23%, 8.87% and 9.85%
respectively. The larger firms with more total assets like energy sector and transportation sectors do
have average growth rates of a slighter more than 10%. Our data support the traditional notion that
smaller firms usually have more growth opportunities. It is evident from the descriptive statistics that
relatively smaller firms of cables and electric sector and engineering sector have maximum growth
rates among whole of the sample firms.
It is further noticed that firms with more growth opportunities also have higher leverage ratio during
whole of the window period. The greatest leverage ratio is for the cables sector i.e. 17.08 whereas
engineering sector also has third highest leverage ration among all sixteen non-financial public limited
firms. It may be argued that firms with higher growth opportunities used to cater their financing needs
from the external sources of funds most of the times which is a quick way to raise funds. These firms
can fulfill their financing needs either by short term fund raising or by issuing long term debt securities
46
instead of being involved in complicated and procedures of equity offering. One more argument in this
regard may be that due to agency problem, managers of high growth firms issue debt to fully capitalize
on the future profits expected from the higher sales in coming years for their own individual benefits.
Table 4.1 also depicts some information regarding performance of 204 sampled industrial firms of the
study. These performance measures include two accounting measures of returns based on book value
data whereas two other measures are being calculated from the market data for the period of 19982005. The average return on assets for non-financial firms in Pakistan is 5.66%. There are only six
sectors which are yielding more than this benchmark. The highest performing sector on the basis of
ROA is paper and board with ROA of 10.28% followed by glass and ceramics with ROA of 8.73%.
The engineering and cables & electric sectors are among the lowest returns generating sectors which
were supposed to be the sectors with the maximum growth opportunities. However, the smaller firms
are still able to earn more profits as compared to larger firms i.e. glass and ceramics; the smallest sector
of the sample. The ROA is also not subject to major variations as the standard deviation of returns is
relatively lower for almost all the sectors. These yielding performances are more or less same for the
second book-based performance variable Return on Equity (ROE).
57However, the situation is a little bit different when we talk about the market-based return measures
i.e. Market Rate of Returns (MRR) and Tobin’s q which are reported in Panel C of Table 4.1.
Engineering sectors which was not performing on book valued measure of returns of ROA and ROE
became the highest earning sector in the market. The investors are giving more value to sectors which
are having more growth opportunities i.e. engineering, cables, textile composite sectors which also
depicts the speculative behavior of the Pakistani investors. However, there is also a sector which is
performing at the lowest among all the sectors i.e. Leather and tanneries having only 18.84% growth in
market share price as compared to the previous years.
Tobin’s q is a performance measure which is being used by researchers studying the stock markets
behaviors. The use of this measure is extended in recent years as a measure comparative to traditional
performance variables. The q is a comparative measure of market value of firms in relation to book
value of firm. A value of q greater than one indicates the strength of the firm in the market. The
average q of our sample is calculated as 1.13 which means that market value of firms in Pakistani
market, on average, is greater than their respective book value. However, there are various industrial
sectors in Pakistan having q value less than one; in our sample half of total sample. The highest
47
performing sector on the basis of Tobin’s q is food and allied sector where investors are believing that
its value should be more than double of its book value. It is also the sector with the greatest variation in
performance where once it has produced a q value of 11.92 even.
4.2 Analysis of Variance (ANOVA)
Difference in the relative degree of aggressive/conservative investment policies across industries has
been testes through one-way ANOVA and results are presented in Table 4.2. The resulting value of Ftest is 7.639 which is significant at 1% level indicates that a significant difference exists between the
industry practices relating to aggressive/conservative investment policies. To further examine the
strength of results of ANOVA, some post hoc tests has also been applied like Least Significant
Difference (LSD), Tukey’s HSD and Bonferroni test to compare the industry mean values of TCA/TA
on a paired sample basis. Tukey’s HSD test has also been used by Weinraub and Visscher (1998) to
examine differences in working capital policies. The results are presented in Panel A, B, C of Table 4.2
respectively. For Least Significant Difference (LSD), among 120 pairs, 69 pairs are statistically
significant at difference levels of significance [Panel A]. These results are further supported by Tukey’s
HSD and Bonferroni test in Panel B and C of Table 4.2 respectively. It is apparent from both ANOVA
and all three post hoc tests for variance that significant differences exist among the various industrial
groups regarding investment working capital management policies. The different firms in different
industrial group might have different policy regarding maintenance of its working capital investment
policy.
ANOVA and the three post hoc tests have also been applied to TCL/TA ratio to examine the
differences in financing policies among industries over the study period. The results are presented in
Table 4.3 [Panel A, B, C]. The significant F-statistics clearly indicates the existence of statistically
significant differences among industries regarding financing working capital policies. Panel A (Least
Significant Difference Test) of Table 4.3 also shows 66 pairs of industries that are significant at
different level of significant. It is clear now that significant industry differences do exist in the relative
degree of both aggressive/conservative working capital investment and financing policies. However,
both the ANOVA and three post hoc tests, including Test of Least Significance Difference (LSD),
Tukey’s HSD and Bonferroni Test, show these differences are generally broader and more significant
when examining working capital investment policies. In the light of results presented in Table 4.2 and
4.3, we can accept our first two hypotheses i.e. H1 and H2, which state the significant differences
among the working capital management practices across different industries.
48
TABLE 4.2 (Panel A)
Results of ANOVA (F-test) and Test of Least Significant Differences (LSD) for Total Current Assets / Total Assets (TCA / TA)
F Statistics = 7.639***
Industries
Auto
Cabl. &
Elec.
Cement
Chem &
Pharma.
Engin.
Food
Fuel &
Energy
Glass
& Cer.
Leather
Pap. &
Board
Sugar
Syn.
&
Rayon
Tex.
Comp.
Tex.
Spinning
Tex.
Weaving
Auto & Allied
--
Cabl. & Elec.
-0.067
--
0.430***
0.496***
--
Chem & Pharma
0.046
0.113
-0.39***
--
Engineering
0.113*
0.180*
-0.32***
0.067
--
Food
0.103*
0.169**
-0.33***
0.057
-0.011
--
Fuel & Energy
0.165***
0.231***
-0.26***
0.119**
0.051
0.062
--
Glass & Cer.
0.197**
0.264***
-0.23***
0.151*
0.083
0.094
0.032
--
Leather
-0.126
-0.060
-0.56***
-0.172*
-0.24**
-0.23**
-0.3***
-0.3***
--
Pap. & Brd.
0.108
0.175*
-0.32***
0.062
-0.005
0.005
-0.056
-0.089
0.235**
--
Sugar
0.284***
0.351***
-0.15***
0.238***
0.171***
0.181***
0.119**
0.087
0.410***
0.176***
--
Syn. & Rayon
0.265***
0.332***
-0.16***
0.219***
0.151**
0.162***
0.100
0.068
0.391***
0.157**
-0.019
--
Tex. Comp.
0.202***
0.269***
-0.23***
0.156***
0.089
0.099**
0.038
0.005
0.328***
0.094
-0.08*
-0.063
--
Tex. Spinning
0.209***
0.276***
-0.22***
0.164***
0.096
0.107**
0.045
0.013
0.335***
0.101*
-0.07*
-0.055
0.007
--
Tex. Weaving
0.183**
0.250
-0.25***
0.137*
0.070
0.081
0.019
-0.014
0.309***
0.075
-0.101
-0.082
-0.019
-0.026
--
Trans.&Comm.
0.282***
0.349***
-0.148*
0.236***
0.169*
0.179**
0.118
0.085
0.409***
0.174**
-0.002
0.017
0.080
0.073
-0.099
Cement
*** Significant at 1 % level
** Significant at 5 % level
49
*Significant at 10 % level
TABLE 4.2 (Panel B)
Results of ANOVA (F-test) and Tukey’s HSD for Total Current Assets / Total Assets (TCA / TA)
F Statistics = 7.639***
Industries
Auto
Cabl. &
Elec.
Cement
Chem &
Pharma.
Engin.
Food
Fuel &
Energy
Glass
& Cer.
Leather
Pap.
&
Board
Sugar
Syn.
&
Rayon
Tex.
Comp.
Tex.
Spinning
Tex.
Weaving
Auto & Allied
--
Cabl. & Elec.
-0.067
--
0.430***
0.495***
--
Chem & Pharma
0.046
0.113
-0.38***
--
Engineering
0.113
0.180
-0.31***
0.067
--
Food
0.103
0.169
-0.33***
0.057
-0.011
--
Fuel & Energy
0.165
0.231
-0.26***
0.119
0.051
0.062
--
Glass & Cer.
0.197
0.264
-0.233
0.151
0.083
0.094
0.032
--
Leather
-0.126
-0.060
-0.55***
-0.172
-0.240
-0.229
-0.291
-0.323
--
Pap. & Brd.
0.108
0.175
-0.32***
0.062
-0.005
0.005
-0.056
-0.089
0.235
--
Sugar
0.284***
0.350***
-0.146
0.237***
0.171
0.181*
0.119
0.087
0.410***
0.176
--
Syn. & Rayon
0.265***
0.331**
-0.165
0.219**
0.151
0.162
0.100
0.068
0.391***
0.157
-0.019
--
Tex. Comp.
0.202***
0.268*
-0.23**
0.156*
0.089
0.099
0.038
0.005
0.329**
0.094
-0.082
-0.063
--
Tex. Spinning
0.209***
0.276**
-0.22***
0.163***
0.096
0.107
0.045
0.013
0.336**
0.101
-0.074
-0.055
0.007
--
Tex. Weaving
0.183
0.250
-0.247
0.137
0.070
0.081
0.019
-0.014
0.310
0.075
-0.101
-0.082
-0.019
-0.026
--
0.282**
0.348*
-0.148
0.236
0.169
0.179
0.118
0.085
0.409**
0.174
-0.002
0.017
0.080
0.073
0.099
Cement
Trans.&Comm.
*** Significant at 1 % level
** Significant at 5 % level
50
*Significant at 10 % level
TABLE 4.2 (Panel C)
Results of ANOVA (F-test) and Bonferroni Test for Total Current Assets / Total Assets (TCA / TA)
F Statistics = 7.639***
Industries
Auto
Cabl. &
Elec.
Cement
Chem &
Pharma.
Engin.
Food
Fuel &
Energy
Glass
& Cer.
Leather
Pap.
&
Board
Sugar
Syn.
&
Rayon
Tex.
Comp.
Tex.
Spinning
Tex.
Weaving
Auto & Allied
--
Cabl. & Elec.
-0.067
--
0.430***
0.497***
--
Chem & Pharma
0.046
0.113
-0.38***
--
Engineering
0.113
0.180
-0.32***
0.067
--
Food
0.103
0.169
-0.33***
0.057
-0.011
--
Fuel & Energy
0.165
0.231
-0.27***
0.119
0.051
0.062
--
Glass & Cer.
0.197
0.264
-0.233
0.151
0.083
0.094
0.032
--
Leather
-0.126
-0.060
-0.56***
-0.172
-0.240
-0.229
-0.291
-0.323
--
Pap. & Brd.
0.108
0.175
-0.32***
0.062
-0.005
0.005
-0.056
-0.089
0.235
--
Sugar
0.284***
0.351***
-0.146
0.237***
0.171
0.181*
0.119
0.087
0.410***
0.176
--
Syn. & Rayon
0.265***
0.332**
-0.165
0.219**
0.151
0.162
0.100
0.068
0.391**
0.157
-0.019
--
Tex. Comp.
0.202***
0.269
-0.23***
0.156
0.089
0.099
0.038
0.005
0.329*
0.094
-0.082
-0.063
--
Tex. Spinning
0.209***
0.276*
-0.22***
0.163***
0.096
0.107
0.045
0.013
0.336**
0.101
-0.074
-0.055
0.007
--
Tex. Weaving
0.183
0.250
-0.247
0.137
0.070
0.081
0.019
-0.014
0.310
0.075
-0.101
-0.082
-0.019
-0.026
--
0.282**
0.349*
-0.148
0.236
0.169
0.179
0.118
0.085
0.409**
0.174
-0.002
0.017
0.080
0.073
0.099
Cement
Trans.&Comm.
*** Significant at 1 % level
** Significant at 5 % level
51
*Significant at 10 % level
TABLE 4.3 (Panel A)
Results of ANOVA (F-test) and Test of Least Significant Differences (LSD) for Total Current Liabilities / Total Assets (TCL / TA)
F Statistics = 4.828***
Industries
Auto
Cabl. &
Elec.
Cement
Chem &
Pharma.
Engin.
Food
Fuel &
Energy
Glass
& Cer.
Leather
Pap.
&
Board
Sugar
Syn.
&
Rayon
Tex.
Comp.
Tex.
Spinning
Tex.
Weaving
Auto & Allied
--
Cabl. & Elec.
-0.081
--
0.234***
0.315***
--
Chem & Pharma
0.076*
0.157**
-0.16***
--
Engineering
0.081
0.161**
-0.15***
0.004
--
Food
-0.008
0.072
-0.24***
-0.09**
-0.089
--
Fuel & Energy
0.049
0.130*
-0.19***
-0.027
-0.031
0.058
--
Glass & Cer.
0.188***
0.268***
-0.047
0.111*
0.107
0.196***
0.138**
--
Leather
-0.190**
-0.109
-0.43***
-0.27***
-0.3***
-0.182**
-0.2***
-0.4***
--
Pap. & Brd.
0.183***
0.263***
-0.052
0.107**
0.103
0.192***
0.134**
-0.005
0.37***
--
0.067
0.147**
-0.17***
-0.010
-0.014
0.075*
0.017
-0.12*
0.256***
-0.12*
--
0.132***
0.212***
-0.103*
0.055
0.051
0.140***
0.082
-0.056
0.322***
-0.052
0.065
--
Tex. Comp.
0.031
0.112*
-0.21***
-0.046
-0.050
0.039
-0.018
-0.16**
0.221***
-0.2***
-0.036
-0.10**
--
Tex. Spinning
0.010
0.091
-0.23***
-0.066**
-0.070
0.019
-0.039
-0.2***
0.200***
-0.2***
-0.056
-0.1***
-0.021
--
Tex. Weaving
-0.029
0.052
-0.26***
-0.105*
-0.109
-0.020
-0.078
-0.2***
0.161*
-0.2***
-0.095
-0.16**
-0.060
-0.039
--
Trans.&Comm.
0.126**
0.207**
-0.108*
0.050
0.046
0.135**
0.077
-0.061
0.316***
-0.1***
0.060
-0.005
0.096
0.116**
0.155**
Cement
Sugar
Syn. & Rayon
*** Significant at 1 % level
** Significant at 5 % level
52
*Significant at 10 % level
TABLE 4.3 (Panel B)
Results of ANOVA (F-test) and Tukey’s HSD for Total Current Liabilities / Total Assets (TCL / TA)
F Statistics = 4.828***
Industries
Auto
Cabl. &
Elec.
Cement
Chem &
Pharma.
Engin.
Food
Fuel &
Energy
Glass
& Cer.
Leather
Pap.
&
Board
Sugar
Syn.
&
Rayon
Tex.
Comp.
Tex.
Spinning
Tex.
Weaving
Auto & Allied
--
Cabl. & Elec.
-0.081
--
0.235***
0.315***
--
Chem & Pharma
0.076
0.157
-0.158**
--
Engineering
0.081
0.161
-0.154
0.004
--
Food
-0.008
0.072
-0.24***
-0.085
-0.089
--
Fuel & Energy
0.049
0.130
-0.19***
-0.027
-0.031
0.058
--
Glass & Cer.
0.188
0.268
-0.047
0.111
0.107
0.196
0.138
--
Leather
-0.190
-0.109
-0.43***
-0.266*
-0.270
-0.182
-0.239
-0.4***
--
Pap. & Brd.
0.183
0.264*
-0.052
0.107
0.103
0.192*
0.134
-0.005
0.373***
--
Sugar
0.067
0.147
-0.17**
-0.010
-0.014
0.075
0.017
-0.121
0.256*
-0.117
--
Syn. & Rayon
0.132
0.212
-0.103
0.055
0.051
0.140
0.082
-0.056
0.322**
-0.052
0.065
--
Tex. Comp.
0.031
0.112
-0.21***
-0.046
-0.050
0.039
-0.018
-0.157
0.221
-0.152
-0.036
-0.101
--
Tex. Spinning
0.010
0.091
-0.23***
-0.066
-0.070
0.019
-0.039
-0.178
0.200
-0.17*
-0.056
-0.122
-0.021
--
Tex. Weaving
-0.029
0.052
-0.27***
-0.105
-0.109
-0.020
-0.078
-0.217
0.161
-0.212
-0.095
-0.160
-0.060
-0.039
--
Trans.&Comm.
0.126
0.207
-0.108
0.050
0.046
0.135
0.077
-0.061
0.316*
-0.057
0.060
-0.005
0.096
0.116
0.155
Cement
*** Significant at 1 % level
** Significant at 5 % level
53
*Significant at 10 % level
TABLE 4.3 (Panel C)
Results of ANOVA (F-test) and Bonferroni Test for Total Current Liabilities / Total Assets (TCL / TA)
F Statistics = 4.828***
Industries
Auto
Cabl. &
Elec.
Cement
Chem &
Pharma.
Engin.
Fuel &
Energy
Food
Glass
& Cer.
Leather
Pap.
&
Board
Sugar
Syn.
&
Rayon
Tex.
Comp.
Tex.
Spinning
Tex.
Weaving
Auto & Allied
--
Cabl. & Elec.
-0.081
--
0.235***
0.315***
--
Chem & Pharma
0.076
0.157
-0.158*
--
Engineering
0.081
0.161
-0.154
0.004
--
Food
-0.008
0.072
-0.24***
-0.085
-0.089
--
Fuel & Energy
0.049
0.130
-0.19**
-0.027
-0.031
0.058
--
Glass & Cer.
0.188
0.268
-0.047
0.111
0.107
0.196
0.138
--
Leather
-0.190
-0.109
-0.43***
-0.266*
-0.270
-0.182
-0.239
-0.4***
--
Pap. & Brd.
0.183
0.264
-0.052
0.107
0.103
0.192
0.134
-0.005
0.373***
--
Sugar
0.067
0.147
-0.17**
-0.010
-0.014
0.075
0.017
-0.121
0.256
-0.117
--
Syn. & Rayon
0.132
0.212
-0.103
0.055
0.051
0.140
0.082
-0.056
0.322**
-0.052
0.065
--
Tex. Comp.
0.031
0.112
-0.21***
-0.046
-0.050
0.039
-0.018
-0.157
0.221
-0.152
-0.036
-0.101
--
Tex. Spinning
0.010
0.091
-0.23***
-0.066
-0.070
0.019
-0.039
-0.178
0.200
-0.173
-0.056
-0.122
-0.021
--
Tex. Weaving
-0.029
0.052
-0.26***
-0.105
-0.109
-0.020
-0.078
-0.217
0.161
-0.212
-0.095
-0.160
-0.060
-0.039
--
Trans.&Comm.
0.126
0.207
-0.108
0.050
0.046
0.135
0.077
-0.061
0.316*
-0.057
0.060
-0.005
0.096
0.116
0.155
Cement
*** Significant at 1 % level
** Significant at 5 % level
54
*Significant at 10 % level
4.3 Rank Order Correlation
Once the significance differences for working capital investment and financing policies are explored
across industries, next to examine was the relative stability of these differences over the study period.
For this purpose, a mean industry value for TCA/TA has been calculated for each industry for each
year and ranked from the highest to lowest ratio. Then the base year (1998) rankings were sequentially
compared to the TCA/TA rankings of each succeeding year. The industries were also ranked for each
year on the basis of Total Current Liabilities / Total Assets and their rankings were also compared with
the base year of 1998. The rank order correlation coefficients and their respective Z-values are
presented in Table 4.4. The correlation between the base year and each of the succeeding year is high
as well as highly statistically significant for both of the working capital management policies i.e.
working capital investment policy and working capital financing policy.
TABLE 4.4
Rank Order Correlations for Working Capital Management Policies
TCA / TA
TCL / TA
Between Base Year
and:
Year
Correlation
Z Value
Correlation
Z Value
Year 2
0.953
3.690***
0.959
3.713***
Year 3
0.876
3.395***
0.800
3.098***
Year 4
0.891
3.452***
0.868
3.360***
Year 5
0.874
3.383***
0.859
3.326***
Year 6
0.859
3.326***
0.829
3.213***
Year 7
0.944
3.656***
0.909
3.519***
Year 8
0.941
3.645***
0.859
3.326***
*** Significant at 1 % level
It is evident from the results that each industry maintained its relative degree of aggressiveness for both
working capital investment (TCA/TA) and financing (TCL/TA) policies over time. There is strong
correlation between the base year rankings and succeeding year rankings for both the policies.
Furthermore, these correlation values are statistically significant at 1% level. This means not only
55
significance differences among working capital management policies exist but also they persist over
the longer period of time. Therefore, we can accept our third hypotheses H3 that working capital
policies are relatively stable over time.
Moreover, the relationship between the working capital investment and financing policies is also
examined in this study. The objective was to determine how an aggressive investment policy
corresponds to aggressive financing policy. To validate this relationship, a year-by-year analysis has
been conducted. Industries are ranked from low to high TCA/TA ratios for the first year, an ascending
order of degree of aggressiveness for working capital investment policy. Sample industries are also
ranked from high to low TCL/TA ratios corresponding to an ascending order of aggressiveness of
working capital financing polices. Rank order correlation has been performed on these policies for first
year and all succeeding years subsequently.
TABLE 4.5
Year-wise Rank Correlation of Working Capital Management Policies
Year
Correlation
Z Value
1998
-0.697
2.700**
1999
-0.791
3.064***
2000
-0.665
2.574**
2001
-0.579
2.244**
2002
-0.591
2.290**
2003
-0.497
1.925**
2004
-0.671
2.597**
2005
-0.682
2.643**
*** Significant at 1 % level
** Significant at 5 % level
* Significant at 10 % level
The results are presented in Table 4.5. All the coefficients of rank order correlation are positive and
statistically significant at 5% except year 1999, which is significant at 1% level of significance. The
negative correlation between the investment and financing policies indicate the industries, which
follow aggressive investment working capital policies, those simultaneously follow conservative
working capital financing policies to balance the aggressive impact of investment policies. Same is the
case where once a conservative investment policy to working capital management is adopted, that is
56
compensated by having an aggressive approach to working capital asset management. Therefore, H4
and H5 are also accepted which states that an aggressive investment policy is accompanied by an
aggressive financing policy on the other hand and vice versa.
4.4 Regression Analysis
Finally, impact of aggressive/conservative working capital management policies on profitability of
firms has been examined by estimating linear regression models # 1-8. The mean TCA/TA values of
each firm along with the control variables for eight years has been regressed against mean values of
performance variables i.e. ROA, ROE, MRR, Tobin’s q in individual regression models. The combined
results are presented in Panel A of Table 4.6.
Table 4.6 [Panel A] reports the result of regression model in which the impact of working capital
investment policy on four performance measurements has been examined. The F-values of regression
models are found statistically significant whereas Durbin-Watson statistics of more than 1.7 indicating
less correlation between the independent variables of all the four regressions models. The t-statistics of
working capital investment policy is positive and statistically significant at 1%level for Return on
Assets and Tobin’s q. However, Return on Equity and Market Rate of Returns is not found to be
statistically correlated with the working capital investment management of firms. The positive
coefficient of TCA/TA indicates a negative relationship between the degree of aggressiveness of
investment policy and return on assets. As the TCA/TA increases, degree of aggressiveness decreases,
and return on assets increases. Therefore, there is negative relationship between the relative degree of
aggressiveness of working capital investment policies of firms and both performance measures i.e.
ROA and Tobin’s q. This similarity in market and accounting returns confirms the notion that investors
do not believe in the aggressive approach of working capital management, hence, they don’t give any
additional value to the firms in Karachi Stock Exchange.
The control variables used in the regression models are natural log of average size, average sales
growth and average leverage. All the control variables have their impact on the performance of the
firms; however, their impact is limited to performance variables based on the book values i.e. return on
assets and return on equity. Firm’s growth and size cause the returns of the firms to be increased and it
is statistically significant. This phenomenon confirms the notion that leverage, growth and size are
strongly correlated with the book value based performance measures (Deloof 2003; Eljelly 2004).
57
Table 4.6 (Panel A)
Regression Analysis of Average Performance Measures &
Working Capital Investment Policy
A(TCA/TA)
ASIZE
AGROWTH
ALVRG
Year
F-Value
D-W
R2
β
t-value
β
t-value
β
t-value
β
t-value
AROA
.215
3.316***
.171
2.621***
.271
3.039***
-.428
-4.814***
9.894***
1.967
.166
AROE
.029
1.081
.055
2.035**
-.432
-11.74***
-.576
-15.67***
299.31***
1.686
.857
AMRR
-.089
-1.282
.015
.217
.063
.654
.119
1.249
1.947*
1.987
.038
A Tobin’s q
.190
2.735***
.125
1.798*
.009
.090
.001
.005
2.522**
1.936
.048
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
A (TCA / TA) = Average Working Capital Investment Policy for the period of 1998-2005
ASIZE = Average Size of firms measured by the natural log of total assets for the period of 1998-2005
AGRWOTH = Average Sales Growth of firms for the period of 1998-2005
ALVRG = Firms’
Average leverage level for the period of 1998-2005
58
Table 4.6 (Panel B)
Regression Analysis of Average Performance Measures &
Working Capital Financing Policy
A(TCL/TA)
Year
ASIZE
AGROWTH
ALVRG
F-Value
D-W
R2
β
t-value
β
t-value
β
t-value
β
t-value
ROA
-.197
-.2878***
.141
2.151**
.201
2.175**
-.344
-3.672***
9.124***
1.742
.155
ROE
.094
3.446***
.059
2.265**
-.403
-10.91***
-.614
-16.37***
317.98***
1.733
.865
MRR
-.112
-1.537
.014
.195
.029
.298
.164
1.644*
2.132*
1.971
.041
Tobin’s q
.171
2.334**
.123
1.766*
.057
.583
-.067
-.673
2.008*
1.879
.039
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
A (TCL / TA) = Average Working Capital Financing Policy for the period of 1998-2005
ASIZE = Average Size of firms measured by the natural log of total assets for the period of 1998-2005
AGRWOTH = Average Sales Growth of firms for the period of 1998-2005
ALVRG = Firms’ Average leverage level for the period of 1998-2005
59
Panel B of Table 4.6 reports regression results for working capital financing policy and the four
performance measures. The F-value of all four regression models and Durbin-Watson statistics
indicating same results as we have in Panel A. The relationship between the control variables and
performance measures is also same as discussed earlier. The negative value of β coefficient for
TCL/TA also points out the same negative relationship between the aggressiveness of working capital
financing policy and return on assets. Higher the TCL/TA ratio, more aggressive the financing policy,
that yields negative return on assets.
However, the relationship between working capital financing policy and return on equity is found to be
positive and statistically significant. Moreover, the firms are aggressive in terms of working capital
financing policy, higher the TCL/TA ratio, yet higher the return on equity. This has an obvious reason;
when firms are more aggressive towards managing finances of short term, they use more current liabilities
as a percentage of total assets. Hence, they are using less long term debt and shareholders equity, which
ultimately increases the return on equity. But, surprisingly, the relationship between Tobin’s q and working
capital financing policy has been established as positive and statistically significant. Investors in the stock
exchange are giving more value to the firms which are adopting an aggressive approach towards working
capital financing policy so these firms are producing higher q value.
To have a more lucid insight into the relationships established, the year analysis has also been
conducted by running cross-sectional regressions for each of the year from 1998-2005 for both working
capital investment and financing policy. For each year, independent variables have been regressed
against performance measures and results are reported in Table 4.7 to 4.10 indicating the impact of
working capital policies on the profitability of firms in Pakistan. The model F-values and the DurbinWatson statistics indicate overall best fit of the model.
Table 4.7 [Panel A] indicate regression analysis of return on assets and working capital investment
policy for each of eight years of study from 1998-2005. The positive and significant β values confirms
our previous results that there is a negative correlation between the degree of aggressiveness of
working capital investment policy and return on assets. There are two years i.e. 1998 and 2004, for
which results are not statistically significant; however, the relationship was same as found earlier. Panel
B also confirms the same negative correlation between the working capital financing policy and return
on assets. Table 4.8 takes into consideration year-wise regression analysis of working capital policies
and return on equity. Panel A & B also confirms the findings of Table 4.6 [Panel B] that there is
60
positive and significant correlation between working capital policies and return on equity. However,
results are broader for return on assets as compared to return on equity from the statistical significance
point of view. Though, the results are statistically less impressive for ROE, which is apparent from the
low level of significance of β coefficients and t-values, however, we can predict a positive relationship
between the degree of aggressiveness of working capital policies and return on equity.
Table 4.9 and 4.10 provide investigation into the relationship of working capital policies and two
market value based firm’s performance measures. Results of regression analysis of market rate of
return and working capital investment and financing policy are reported in Panel A & B of Table 4.9
respectively. Supporting the earlier findings, market rate of returns failed to relate with either working
capital investment policy or working capital financing policy. The only exception was the year 2004
where the β’s were found to be negatively and significantly correlated with working capital variables;
however, the results based on market rate of returns can not be generalized for whole of the sample
period.
Lastly, Table 4.10 shows the year-wise regression analysis of Tobin’s q; another market value based
performance variable; and working capital policies of firms for the period of 1998-2005. Yet again the
results are in accordance with our earlier findings and pose a negative relationship between
aggressiveness of working capital investment policy and q as well as a positive relationship between
degree of aggressiveness of working capital financing policy and Tobin’s q. As the level of current
assets and current liabilities in the balance sheet of a firm increases, the short term repaying capacity of
firms increase, investors perceive the firm a better performer; hence they give more value to the firm
which leads the q value of firm greater than one.
To further validate the above-mentioned results, the impact of working capital investment and working
capital financing policy on performance variables has also been examined on large set of data. For this
purpose, panel data regression analysis has been used. The panel data set has been developed for eight
years of study and 204 sampled firms which produced 1632 year end observations. In the panel data
regression, to control the economic influence on performance of firms, real GDP growth rate (GDPGR)
has also been included as a control variable. According to the results reported in Table 4.11, model Fvalues are found statistically significant and Durbin-Watson indicates less autocorrelation between the
independent variables. The results are again confirming our previous results and report a significant
61
Table 4.7 (Panel A)
Year-wise Regression Analysis of Return on Assets (ROA) &
Working Capital Investment Policy
TCA/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-1.144
1.153
1.909
.023
-.276
-4.599***
19.245***
1.986
.281
.573
-.326
-4.898
9.238***
1.857
.157
.099
1.444
-.008
-.118
3.914***
1.941
.073
1.561
.277
4.268***
-.253
-3.874***
11.775***
2.094
.191
.221
3.463***
.129
2.013**
-.168
-2.619***
11.924***
1.811
.193
.308
.100
1.427
-.002
-.026
-.117
-1.65*
1.240
1.918
.024
4.999***
.243
3.763***
.048
.754
-.195
-3.035***
11.785***
1.806
.192
β
t-value
β
t-value
β
t-value
β
t-value
1998
.043
.615
.098
1.395
.057
.414
-.157
1999
.201
3.319***
-.063
-1.046
.409
6.79***
2000
.240
3.658***
-.057
-.857
.038
2001
.250
3.661***
.045
.651
2002
.149
2.322**
.101
2003
.319
5.007***
2004
.022
2005
.321
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCA / TA = Working Capital Investment Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
62
Table 4.7 (Panel B)
Year-wise Regression Analysis of Return on Assets (ROA) &
Working Capital Financing Policy
TCL/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-.932
2.019*
1.887
.039
-.235
-3.642***
17.306***
1.889
.258
.588
-.278
-3.669***
5.649***
1.822
.102
.096
1.382
.002
.003
1.574
1.854
.031
1.181
.287
4.453***
-.184
-2.661***
12.176***
1.955
.197
.192
2.909***
.148
2.225**
-.129
-1.906*
7.968***
1.544
.138
-2.502***
.093
1.353
.026
.370
-.088
-1.256
2.818**
1.914
.054
-3.858***
.210
3.196***
.084
1.283
-.116
-1.709*
9.011***
1.731
.153
β
t-value
β
t-value
β
t-value
β
t-value
1998
-.136
-1.942
.087
1.241
.030
.219
-.127
1999
-.138
-2.140**
-.089
-1.452
.392
6.416***
2000
-.050
-.675
-.075
-1.097
.040
2001
-.142
-2.035**
.020
.288
2002
-.176
-2.593**
.077
2003
-.221
-3.270***
2004
-.178
2005
-.261
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCL / TA = Working Capital Financing Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
63
Table 4.8 (Panel A)
Year-wise Regression Analysis of Return on Equity (ROE) &
Working Capital Investment Policy
TCA/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-6.732***
27.456***
1.984
.356
-.186
-3.029***
16.235***
1.981
.246
.558
-.467
-7.450***
16.616***
1.591
.251
.102
1.545
-.264
-3.963***
7.094***
2.010
.125
1.83*
.210
3.246***
-.307
-4.707***
11.906***
2.414
.193
.253
3.93***
.065
1.001
-.214
-3.312***
10.961***
1.777
.181
-.099
.102
1.439
-.030
-.426
-.033
.461
0.627
1.933
.012
4.072***
.244
4.012***
.098
1.620*
-.385
-6.346***
19.135***
1.960
.278
β
t-value
β
t-value
β
t-value
β
t-value
1998
.233
4.070***
.183
3.199***
.266
2.384**
-.749
1999
.191
3.092***
-.019
-.306
.428
6.932***
2000
.234
3.788***
-.009
-.142
.035
2001
.225
3.387***
.052
.782
2002
.172
2.683***
.119
2003
.265
4.125***
2004
-.007
2005
.248
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCA / TA = Working Capital Investment Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
64
Table 4.8 (Panel B)
Year-wise Regression Analysis of Return on Equity (ROE) &
Working Capital Financing Policy
TCL/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-6.172***
22.239***
1.896
.309
-.189
-2.846***
13.211***
1.974
.210
.622
-.526
-7.485***
14.535***
1.603
.226
.102
1.496
-.254
-3.719***
4.290***
1.946
.079
1.917**
.229
3.555***
-.369
-5.340***
12.344***
2.383
.199
.239
3.558***
.077
1.142
-.215
-3.123***
6.237***
1.653
.111
-.727
.101
1.435
-.023
-.328
.043
.605
.759
1.936
.015
-.330
.219
3.468***
.117
1.859*
-.372
-5.691***
13.871
1.887
.218
β
t-value
β
t-value
β
t-value
β
t-value
1998
-.084
-1.413
.160
2.704***
.242
2.087**
-.714
1999
.003
.048
-.033
-.529
.415
6.587***
2000
.192
2.768***
-.010
-.161
.039
2001
.071
1.044
.046
.668
2002
.200
2.943***
.124
2003
-.031
-.454
2004
-.053
2005
-.021
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCL / TA = Working Capital Financing Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
65
Table 4.9 (Panel A)
Year-wise Regression Analysis of Market Rate of Returns (MRR) &
Working Capital Investment Policy
TCA/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-
-
-
-
-.006
-.089
.921
1.641
.018
-1.770*
-.083
-1.160
1.767
2.102
.034
.004
.058
-.059
-.878
5.500***
1.899
.100
.533
.249
3.575***
.041
.591
3.697***
1.983
.069
.080
1.142
.133
1.899**
-.035
-.501
1.948*
1.784
.038
-2.113**
-.066
-1.00
.016
.247
.306
4.575***
7.353***
2.165
.129
.518
-.029
-.412
.088
1.238
.030
.427
.606
1.982
.012
β
t-value
β
t-value
β
t-value
β
t-value
1998
-
-
-
-
-
-
-
1999
.046
.658
.093
1.320
.090
1.278
2000
-.077
-1.091
.045
.643
-.124
2001
.040
.596
-.300
-4.429***
2002
.054
.783
.037
2003
.108
1.550
2004
-.142
2005
.037
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCA / TA = Working Capital Investment Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
66
Table 4.9 (Panel B)
Year-wise Regression Analysis of Market Rate of Returns (MRR) &
Working Capital Financing Policy
TCL/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-
-
-
-
-.020
-.269
.891
1.651
.018
-1.798*
-.048
-.609
1.876*
2.086
.036
.003
.052
-.058
-.861
5.471***
1.905
.099
.519
.254
3.659***
.032
.435
3.585***
1.969
.067
.069
.984
.140
1.989**
-.019
-.258
1.747
1.737
.034
-2.838***
-.059
-.894
.030
.451
.361
5.402***
8.360***
2.265
.144
.260
-.033
-.469
.089
1.264
.026
.356
.556
2.000
.011
β
t-value
β
t-value
β
t-value
β
t-value
1998
-
-
-
-
-
-
-
1999
.042
.564
.093
1.313
.088
1.256
2000
-.098
-1.271
.043
.614
-.126
2001
-.034
-.502
-.305
-4.491***
2002
.032
.442
.036
2003
-.091
-1.273
2004
-.192
2005
.019
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCL / TA = Working Capital Financing Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
67
Table 4.10 (Panel A)
Year-wise Regression Analysis of Tobin’s Q &
Working Capital Investment Policy
TCA/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-.097
3.126**
1.923
.059
.081
1.176
2.289*
1.847
.044
-.453
.042
.591
2.568**
1.918
.049
-.033
-.483
.006
.081
3.149**
1.847
.060
1.904**
-.025
-.360
.030
.424
2.866**
1.971
.054
.106
1.532
-.037
-.534
.004
.059
2.523**
1.941
.048
1.901**
.084
1.201
-.113
-1.603
-.036
-.517
1.848
1.976
.036
2.181**
.086
1.211
-.018
-.250
-.015
-.213
1.454
1.978
.028
β
t-value
β
t-value
β
t-value
β
t-value
1998
.229
3.311***
.073
1.053
.065
.484
-.013
1999
.160
2.297**
.114
1.643*
-.035
-.505
2000
.152
2.176**
.150
2.147**
-.032
2001
.208
3.019***
.133
1.917**
2002
.193
2.790***
.134
2003
.196
2.830***
2004
.135
2005
.154
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCA / TA = Working Capital Investment Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
68
Table 4.10 (Panel B)
Year-wise Regression Analysis of Tobin’s Q &
Working Capital Financing Policy
TCL/TA
Year
SIZE
GROWTH
LVRG
F-Value
D-W
R2
-.345
4.270***
1.854
.079
.016
.225
2.900**
1.792
.055
-.402
-.024
-.313
2.897**
1.891
.055
-.033
-.483
.018
.259
4.735***
1.761
.087
1.954**
-.005
-.071
-.031
-.417
2.747**
1.892
.052
.101
1.432
-.030
-.422
-.015
-.212
.690
1.902
.014
.026
.072
1.1018
-.100
-1.407
-.056
-.781
.928
1.964
.018
-.371
.070
.979
-.005
-.073
-.004
-.048
.293
1.994
.006
β
t-value
β
t-value
β
t-value
β
t-value
1998
.270
3.937***
.072
1.051
.107
.803
-.046
1999
.202
2.770***
.117
1.693*
-.039
-.567
2000
.188
2.453***
.154
2.201**
-.028
2001
.266
3.918***
.143
2.087**
2002
.199
2.705***
.138
2003
.063
.869
2004
.002
2005
-.072
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCL / TA = Working Capital Financing Policy
SIZE = Size of firms measured by the natural log of total assets
GRWOTH = Sales Growth of firms
LVRG = Firms’ leverage level
69
Table 4.11 (Panel A)
Panel Data Regression Analysis of Performance Measures &
Working Capital Investment Policy
ROA
Year
ROE
MRR
Tobin’s q
β
t-value
β
t-value
β
t-value
β
t-value
TCA/TA
.158
6.506***
.172
7.273***
.007
.259
.149
6.156***
SIZE
.082
3.363***
.115
4.792***
-.001
-.052
.092
3.771***
GROWTH
.137
3.805
.143
4.073***
.032
1.204
-.004
-.104
GDPGR
.043
1.759*
.049
2.057**
.094
3.504***
.162
6.627***
LVRG
-.202
-5.606***
-.324
-9.220***
.031
1.169
.004
.101
F-Value
17.166***
35.457***
3.276***
19.245***
D-W
1.875
1.873
1.796
1.948
.050
.098
.011
.056
2
R
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCA / TA = Panel data of working Investment Financing Policy for the period of 1998-2005
SIZE = Panel data of size of firms measured by the natural log of total assets for the period of 1998-2005
GRWOTH = Panel data sales Growth of firms for the period of 1998-2005
LVRG = Panel data of Firms’ leverage level for the period of 1998-2005
70
Table 4.11 (Panel B)
Panel Data Regression Analysis of Performance Measures &
Working Capital Financing Policy
ROA
Year
ROE
MRR
Tobin’s q
β
t-value
β
t-value
β
t-value
β
t-value
TCL/TA
-.171
-6.940***
-.013
-.524
-.044
-1.628
.087
3.506***
SIZE
.064
2.630***
.103
4.24***
-.004
-.143
.087
3.522***
GROWTH
.116
3.204***
.145
4.029***
.033
1.252
.011
.312
GDPGR
.011
.440
.036
1.473
.087
3.235***
.163
6.578***
LVRG
-.168
-4.628***
-.321
-8.908***
.040
1.485
-.013
-.354
F-Value
18.363***
24.151***
3.8***
13.946***
D-W
1.822
1.843
1.799
1.923
.053
.069
.013
.041
2
R
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
TCA / TA = Panel data of working Investment Financing Policy for the period of 1998-2005
SIZE = Panel data of size of firms measured by the natural log of total assets for the period of 1998-2005
GRWOTH = Panel data sales Growth of firms for the period of 1998-2005
LVRG = Panel data of Firms’ leverage level for the period of 1998-2005
71
positive relation between working capital investment policy and return on assets [Panel A]. A
significant positive relationship is also found for return on equity and Tobin’s q. Real GDP growth may
not be affecting the returns based on book values; however, investors working in the economy may
react positively to a positive change in the level of economic activity which is in accordance with the
findings of Lamberson (1995). On the other hand, as per our expectations, working capital financing
policy indicating a significant negative relationship with return on assets and a significant positive
relationship with Tobin’s q.
On the basis of the regression analysis, we can partially accept our hypothesis H6, that an aggressive
working capital policy is inversely related with firm’s profitability. Managers cannot create value if
they are adopting for an aggressive approach towards working capital investment and working capital
financing policy. However, if firms are having aggressive approach to manage the short term liabilities,
investors give more value to those firms in stock markets.
Finally, to empirically test the theory of Belt (1979) and Van-Horne and Wachowicz (2004), impact of
working capital policies on risk of the firm have been investigated by regressing the ordinary least
square regressions for equations 9-18. The risk is measured by the standard deviation of sales and four
return measures as operating and financial risk respectively. The standard deviation has been estimated
over the eight years from 1998 to 2005 and then eight regressions have been run for working capital
investment and working capital financing policy respectively and result are reported in Table 4.12. The
positive β coefficients of σSales, σROA, σROE and σTobin’s q indicate negative relationship between the risk
measurements and the degree of aggressiveness of working capital investment policy. On the other
hand, mixed results have been found for the working capital financing policy. The increased variation
in sales and profitability is attributed to increasing the level of current assets and decreasing the level of
current liabilities in the firm. However, these results are not statistically significant except the σSales in
case of working capital investment policy and for σSales and σTobin’s
q
for working capital financing
policy. In general, we are unable to establish any statistically significant relationship between the level
of current assets and current liabilities and financial risk of Pakistani firms. However, positive
significant results for σSales and working capital policies pose a clear picture that increased level of
current assets would be having a strong positive impact on the operating risk of the firm i.e. variations
in annual sales of the firms.
72
Table 4.12 (Panel A)
Regression Analysis of Risk & Working Capital Investment Policy
A(TCA/TA)
Year
ASIZE
AGROWTH
ALVRG
F-Value
D-W
R2
β
t-value
β
t-value
β
t-value
β
t-value
σRΟΑ
.059
.843
-.165
-2.353**
.049
.515
-.044
-.464
1.699
2.016
.033
σROE
.014
.588
-.026
-1.063
.449
13.589***
.576
17.432***
382.43***
1.849
.885
σMRR
-.023
-.321
-.047
-.669
-.018
-.186
.140
1.447
.935
1.828
.018
σTobin's q
.137
1.956
.053
.757
.118
1.224
-.042
-.435
1.499
1.955
.029
σSales
.170
2.935***
.564
9.712***
.030
.372
-.023
-.290
25.028***
1.782
.335
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
A (TCA / TA) = Average Working Investment Financing Policy for the period of 1998-2005
ASIZE = Average Size of firms measured by the natural log of total assets for the period of 1998-2005
AGRWOTH = Average Sales Growth of firms for the period of 1998-2005
ALVRG = Firms’ Average leverage level for the period of 1998-2005
73
Table 4.12 (Panel B)
Regression Analysis of Risk & Working Capital Financing Policy
A(TCL/TA)
Year
ASIZE
AGROWTH
ALVRG
F-Value
D-W
R2
β
t-value
β
t-value
β
t-value
β
t-value
σRΟΑ
-.073
-.995
-.174
-2.49***
.024
.245
-.014
-.138
1.770
2.056
.034
σROE
-.059
-2.378**
-.031
-1.298
.430
12.796***
.600
17.569
393.44***
1.926
.888
σMRR
.107
1.462
-.038
-.539
.017
.174
.095
.947
1.453
1.839
.028
σTobin's q
.037
.504
.046
.647
.125
1.256
-.055
-.546
.597
1.955
.012
σSales
.225
3.749***
.568
9.902***
.096
1.194
-.113
-1.380
26.989***
1.796
.352
*** Significant at 1% Level
** Significant at 5% Level
* Significant at 10% Level
A (TCL / TA) = Average Working Capital Financing Policy for the period of 1998-2005
ASIZE = Average Size of firms measured by the natural log of total assets for the period of 1998-2005
AGRWOTH = Average Sales Growth of firms for the period of 1998-2005
ALVRG = Firms’ Average leverage level for the period of 1998-2005
74
The above results are contradictory with Gardner et al. (1986), Deloof (2003), Eljelly (2004), Teruel &
Solano (2005) as well as in accordance with Afza and Nazir (2007a, b) and produced negative
relationship between the aggressiveness of working capital policies and accounting measures of
profitability. Although, results of all return variables are significant, however, accounting returns
measures produced broader and consistent results as compared to market rates of returns. Market
returns (MRR) are slightly less significant in our study which is attributed to the more volatile stock
market of Pakistan. The Karachi Stock Market is said to be heavily overvalued stock market, and
hence, the results based on market share price data are more inconsistent. The degree of aggressiveness
of working capital policies is worthwhile only for creating shareholders’ wealth through increasing
market performance whereas accounting performance cannot be increased by being aggressive in
managing of working capital. Moreover, results of Table 4.12 confirmed the results of Carpenter and
Johnson (1983) that there is no statistically significant relationship between the working capital levels
and the financial risk of the firms. However, we found some evidence that working capital policies may
affect the sales volatility of firms during the study period. Our results are somewhat different from
those studies conducted in the developed economies. The Pakistan is one of the emerging economies
and Pakistani markets are much transparent and efficient to fully absorb the impact of information. The
mixed results found in Market Rate of Return (MRR) are the clear example of this state of Pakistani
markets.
75
Chapter 5
CONCLUSION and IMPLICATIONS
Working capital practices are used very often in corporate finance yet its implications are normally
misunderstood. Even among the professional managers, the controversy and confusion persist. While
an accountant will regard working capital as current assets minus current liabilities and call it as net
working capital, a finance manager will consider gross current assets as the working capital. Both may
be true, but their concerns differ. The former’s concern is arithmetical accuracy as he has to tally the
two sides of the balance sheet. But the finance manager’s concern is to find fund for each item of
current assets at such costs and risks that the evolving financial structure remains balanced between the
two. Working capital management is a very important component of financial management because it
directly affects the liquidity and profitability of the company. The managers may adopt the optimal
level of working capital that may maximize the firm’s value. Huge inventory levels and a relaxed trade
credit policy may lead to high sales. Larger inventory reduce the risk of the stock-out trade credit may
stimulate sales because it allows customers to assess product quality before paying (Long et al. 1993).
Due to significant cost advantages for suppliers over financial institutions, it can also be an inexpensive
source of credit for customer (Peterson and Rajan, 1997). On the other side, granting trade credit and
keeping inventories is that money locked up in working capital.
The corporate finance literature has traditionally focused on the study of long-term financial decisions.
Researchers have particularly examined investments, capital structure, dividends or company valuation
decisions, among other topics. However, short-term assets and liabilities are important components of
total assets and needs to be carefully analyzed. Management of these short-term assets and liabilities
necessitates a careful investigation since the working capital management plays an important role for
the firm’s profitability and risk as well as its value (Smith, 1980).
Working capital management policies have been divided into two categories by Weinraub and Visscher
(1998). A firm may adopt an aggressive working capital asset management policy with a low level of
current assets as percentage of total assets. On the other hand, aggressive working capital financing
policy uses high level of current liabilities as percentage of total liabilities. Excessive levels of current
assets may have a negative effect on the firm’s profitability whereas a low level of current assets may
lead to lower level of liquidity and stockouts resulting in difficulties in maintaining smooth operations
76
(Van Horne and Wachowicz 2004). Moreover, aggressive working capital financing policies that utilize
higher levels of normally lower cost short-term debt increase the risk of a short-term liquidity problem.
Therefore, more aggressive working capital policies are associated with higher return and higher risk
while conservative working capital policies are concerned with the lower risk and return (Carpenter and
Johnson 1983, Gardner, Mills and Pope 1986, Weinraub and Visscher 1998).
The present study investigates the relationship of the aggressive and conservative working capital asset
management and financing polices and its impact on profitability of 204 Pakistani firms divided into
sixteen industrial groups by Karachi Stock Exchange for a period of 1998-2005. This research
examines whether significant differences exist among the working capital practices of the firms across
different industries and confirm whether these aggressive or conservative working capital policies are
relatively stable over the period of time. It also validates the relationship between working capital asset
management and financing policies firms and investigates how a working capital asset management
policy corresponds to working capital financing policy. Moreover, the impact of aggressive and
conservative working capital asset management and financing policies profitability of the company by
using various profitability measures based on accounting data and market values. Finally, to confirm
the findings if Carpenter and Johnson (1983), the study has investigated the impact of working capital
policies on the financial and operating risk of firms operating in Pakistan.
In accordance with Weinraub and Visscher (1998), the study found significant differences among their
working capital investment and financing policies across different industries. The nature and adoption
of both the working capital policies vary from industry to industry. Some industries are more
aggressive towards managing their current assets and liabilities whereas there are some industries being
very much conservative in their approach. Moreover, these significant differences are remarkably
stable over the period of eight years of time i.e. 1998-2005. The firms tend to maintain their policies
over the years and there is less probability of being unhinged in working capital management policies.
The positive and significant correlation between the investment and financing policies for industries
indicate that industries which pursue aggressive investment working capital policies also follow
aggressive working capital financing policies. This is contradicting with the findings of Weinraub and
Visscher (1998) that shown a negative correlation between the asset management policies and
financing policies.
77
The impact of aggressive/conservative working capital investment and financing policies has been
examined through ordinary least square regression models between working capital policies and
profitability as well as risk of the firms. For further confirmation, year wise regressions and panel data
regression has also been employed. We found a negative relationship between the profitability
measures of firms and degree of aggressiveness of working capital investment and financing policies.
The firms yield negative returns if they follow an aggressive working capital policy. These results are
further validated by examining the impact of aggressive working capital policies on market measures of
profitability which was not tested before. The results of Tobin’s q were in line of the accounting
measures of profitability and produced almost the same results for working capital investment policy.
However, investors in the stock markets are giving more value to the firms through q if they are more
aggressive in managing their current liabilities. Moreover, we also confirmed the findings of Carpenter
and Johnson (1983) that there is no significant relationship between the aggressiveness or
conservativeness of working capital policies of firms and their operating and financial risk.
As we used a new measure of profitability i.e. Tobin’s q to estimate the relationship of working capital
management and firm returns in Pakistan, the present study is expected to be a significant contribution
in finance literature. Moreover, theoretical discussion on risk and working capital management has also
been tested on empirical basis in an emerging market of Pakistan. Although the results of present study
are in contradiction to some earlier studies on the issue, yet, this phenomenon may be attributed to the
inconsistent and volatile economic conditions of Pakistan. The reasons for this contradiction may
further be explored in upcoming researches and this topic is left for future.
The study also suggests some policy implications for the managers and prospective investor in the
emerging market of Pakistan. Firms with the more aggressive policy towards working capital may not
be able to generate more profitability. So, as far as the book value performance is concerned, managers
can not yield more return on assets and return on equity by following aggressive approach towards
short term assets and liabilities. On the other hand, investors are giving more value to the
aggressiveness of firms towards working capital financing policies. Firms those are using high level of
current liabilities in their financing; their market value is more than the book value. The investors
believe that firms using less equity and less ere mounts of long term loans would be performing better
than the others. However, there are many other factors like agency problem may play a pivotal role in
such firm, and so these factors may further be explored in future.
78
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Appendix I
List of Sample Firms
Sr. #
1
Company Name
Agriautos Industries Ltd
Industry
Automobiles and Allied
2
Al-Ghazi Tractors Ltd
Automobiles and Allied
3
Allwin Engineering Industries Ltd
Automobiles and Allied
4
Atlas Battery Ltd
Automobiles and Allied
5
Atlas Honda Ltd
Automobiles and Allied
6
Automative Battery Co Ltd
Automobiles and Allied
7
Baluchistan Wheels Ltd
Automobiles and Allied
8
Exide Pakistan Ltd
Automobiles and Allied
9
Hino Pak Motors Ltd
Automobiles and Allied
10
Honda Atlas Cars Ltd
Automobiles and Allied
11
Indus Motor Co Ltd
Automobiles and Allied
12
Millat Tractors Ltd
Automobiles and Allied
13
Pak Suzuki Motors Co Ltd
Automobiles and Allied
14
Suzuki Motorcycles Pakistan Ltd
Automobiles and Allied
15
The General Tyre & Rubber Co of Pakistan Ltd
Automobiles and Allied
16
Pak Elektron Ltd
Cables and Electric
17
Pakisan Cables Ltd
Cables and Electric
18
Siemens (Pakistan) Engineering Co. Ltd
Cables and Electric
19
Singer Pakistan Ltd
Cables and Electric
20
Cherat Cement Co Ltd
Cement
21
D.G. Khan Cement Co Ltd
Cement
22
Dadabhoy Cement Industries Ltd
Cement
23
Dadex Eternit Ltd
Cement
24
Al Abbas Cement (Essa Cement) Ltd
Cement
25
Fecto Cement Industrial Ltd
Cement
26
Gharibwal Cement Ltd
Cement
27
Kohat Cement Co Ltd
Cement
28
Lucky Cement Ltd
Cement
86
29
Maple Leaf Cement Factory Ltd
Cement
30
Pakland Cement Ltd
Cement
31
Pioneer Cement Ltd
Cement
32
Abbott Laboratories Pakistan Ltd
Chemicals & Pharmaceuticals
33
Berger Paints Pakistan Ltd
Chemicals & Pharmaceuticals
34
BOC (Pakistan) Ltd
Chemicals & Pharmaceuticals
35
Buxly Paints Ltd
Chemicals & Pharmaceuticals
36
Clariant Pakistan Ltd
Chemicals & Pharmaceuticals
37
Colgate Palmolive (Pakistan) Ltd
Chemicals & Pharmaceuticals
38
Dawood Hercules Chemicals Ltd
Chemicals & Pharmaceuticals
39
Dynea Pakistan Ltd
Chemicals & Pharmaceuticals
40
Engro Chemical (Pakistan)
Chemicals & Pharmaceuticals
41
Fauji Fertilizer Co Ltd
Chemicals & Pharmaceuticals
42
Ferozsons (Laboratories) Ltd
Chemicals & Pharmaceuticals
43
Glaxo Smithkline (Pakistan)
Chemicals & Pharmaceuticals
44
Highnoon (Laboratories) Ltd
Chemicals & Pharmaceuticals
45
ICI Pakistan Ltd
Chemicals & Pharmaceuticals
46
Leiner (Pak) Gelatine Ltd
Chemicals & Pharmaceuticals
47
Otsuka Pakistan Ltd
Chemicals & Pharmaceuticals
48
Pakistan Gum & Chemicals Ltd
Chemicals & Pharmaceuticals
49
Searle Pakistan Ltd
Chemicals & Pharmaceuticals
50
Shaffi Chemical Industries Ltd
Chemicals & Pharmaceuticals
51
Sitara Chemicals Industries Ltd
Chemicals & Pharmaceuticals
52
Wah Noble Chemicals Ltd
Chemicals & Pharmaceuticals
53
Wyeth Pakistan Ltd
Chemicals & Pharmaceuticals
54
Ados Pakistan Ltd
Engineering
55
Bolan Castings Ltd
Engineering
56
Crescent Steel & Allied Products Ltd
Engineering
57
Huffaz Seamless Pipe Industries Ltd
Engineering
58
International Industries Ltd
Engineering
59
KSB Pumps Co Ltd
Engineering
60
Pakistan Engineering Co Ltd
Engineering
87
61
Sazgar Engineering Works Ltd
62
Clover Pakistan Ltd
Food & Allied
63
Ismail Industries Ltd
Food & Allied
64
Kashmir Edible Oil Mills Ltd
Food & Allied
65
Lever Brothers (Pakistan) Ltd
Food & Allied
66
Mitchell's Fruits Farms Ltd
Food & Allied
67
Murree Brewery Co Ltd
Food & Allied
68
National Foods Ltd
Food & Allied
69
Nestle Milkpak Ltd
Food & Allied
70
Noon Pakistan Ltd
Food & Allied
71
Punjab Oil Mills Ltd
Food & Allied
72
Rafhan Bestfoods Ltd
Food & Allied
73
Rafhan Maize Products Ltd
Food & Allied
74
Shezan International Ltd
Food & Allied
75
Wazir Ali Industries Ltd
Food & Allied
76
Attock Refinery Ltd
Fuel & Energy
77
Generteck Pakistan Ltd
Fuel & Energy
78
Haroon Oils Ltd
Fuel & Energy
79
Ideal Energy Ltd
Fuel & Energy
80
Kohinoor Energy Ltd
Fuel & Energy
81
Kohinoor Power Co Ltd
Fuel & Energy
82
Mari Gas Co Ltd
Fuel & Energy
83
National Refinery Ltd
Fuel & Energy
84
Pakistan Oilfields Ltd
Fuel & Energy
85
Pakistan Refinery Ltd
Fuel & Energy
86
Pakistan State Oil Co Ltd
Fuel & Energy
87
Shell Gas LPG (Pakistan) Ltd
Fuel & Energy
88
Shell Pakistan Ltd
Fuel & Energy
89
Sitara Energy Ltd
Fuel & Energy
90
Sui Northern Gas Pipelines Co Ltd
Fuel & Energy
91
Sui Southern Gas Pipline Co Ltd
Fuel & Energy
92
The Hub Power Co Ltd
Fuel & Energy
88
Engineering
93
Frontier Ceramics Ltd
Glass & Ceramics
94
Ghani Glass Ltd
Glass & Ceramics
95
Shabbir Tiles & Ceramics Ltd
Glass & Ceramics
96
Tariq Glass Industries Ltd
Glass & Ceramics
97
Bata Pakistan Ltd
Leather & Tanneries
98
Pakistan Leather Crafts Ltd
Leather & Tanneries
99
Service Industries Ltd
Leather & Tanneries
100
Century Paper & Board Mills Ltd
Paper & Board
101
Cherat Papersack Ltd
Paper & Board
102
Crescent Boards Ltd
Paper & Board
103
Dadabhoy Sack Ltd
Paper & Board
104
Packages Ltd
Paper & Board
105
Pakistan Papersack Corp Ltd
Paper & Board
106
Security Papers Ltd
Paper & Board
107
Adam Sugar Mills Ltd
Sugar & Allied
108
Al-Abbas Sugar Mills Ltd
Sugar & Allied
109
Al-Noor Sugar Mills Ltd
Sugar & Allied
110
Chashma Sugar Mills Ltd
Sugar & Allied
111
Crescent Sugar Mills & Distillery Ltd
Sugar & Allied
112
Faran Sugar Mills Ltd
Sugar & Allied
113
Habib Arkady Ltd
Sugar & Allied
114
Habib Sugar Mills Ltd
Sugar & Allied
115
Haseeb Waqas Sugar Mills Ltd
Sugar & Allied
116
Husein Sugar Mills Ltd
Sugar & Allied
117
JDW Sugar Mills Ltd
Sugar & Allied
118
Kohinoor Sugar Mills Ltd
Sugar & Allied
119
Mehran Sugar Mills Ltd
Sugar & Allied
120
Noon Sugar Mills Ltd
Sugar & Allied
121
Shahmurad Sugar Mills Ltd
Sugar & Allied
122
Shahtaj Sugar Mills Ltd
Sugar & Allied
123
Sind Abadgars Sugar Mills Ltd
Sugar & Allied
124
Tandlianwala Sugar Mills Ltd
Sugar & Allied
89
125
Al-Abid Silk Mills Ltd
Synthetic & Rayon
126
Bannu Woollen Mills Ltd
Synthetic & Rayon
127
Dewan Salman Fiber Ltd
Synthetic & Rayon
128
Gatron (Industries) Ltd
Synthetic & Rayon
129
Ibrahim Fibers Ltd
Synthetic & Rayon
130
Indus Polyester Co Ltd
Synthetic & Rayon
131
Liberty Mills Ltd
Synthetic & Rayon
132
Rupali Polyester Ltd
Synthetic & Rayon
133
The National Silk & Rayon Mills Ltd
Synthetic & Rayon
134
Ahmed Hassan Textile Mills Ltd
Textile Composite
135
Artistic Denim Mills Ltd
Textile Composite
136
Aruj Garments Accessories Ltd
Textile Composite
137
Fateh Textile Mills Ltd
Textile Composite
138
Gul Ahmed Textile Mills Ltd
Textile Composite
139
Husein Industries Ltd
Textile Composite
140
Ishaq Textile Mills Ltd
Textile Composite
141
Kohinoor Textile Mills Ltd
Textile Composite
142
Legler-Nafis Denim Mills Ltd
Textile Composite
143
Masood Textile Mills Ltd
Textile Composite
144
Nishat (Chunian) Ltd
Textile Composite
145
Nishat Mills Ltd
Textile Composite
146
Quetta Textile Mills Ltd
Textile Composite
147
Reliance Weaving Mills Ltd
Textile Composite
148
Safa Textiles Ltd
Textile Composite
149
Sapphire Fibres Ltd
Textile Composite
150
Sapphire Textile Mills Ltd
Textile Composite
151
Shams Textile Mills Ltd
Textile Composite
152
Suraj Cotton Mills Ltd
Textile Composite
153
The Crescent Textile Mills Ltd
Textile Composite
154
Towellers Ltd
Textile Composite
155
Al-Qaim Textile Mills Ltd
Textile Spinning
156
Allahwasaya Textile Mills Ltd
Textile Spinning
90
157
Apollo Textile Mills Ltd
Textile Spinning
158
Ayesha Textile Mills Ltd
Textile Spinning
159
Babri Cotton Mills Ltd
Textile Spinning
160
Bhanero Textile Mills Ltd
Textile Spinning
161
Blessed Textile Mills Ltd
Textile Spinning
162
D.M. Textile Mills Ltd
Textile Spinning
163
Dar-es-Salam Textile Mills Ltd
Textile Spinning
164
Dewan Khalid Textile Mills Ltd
Textile Spinning
165
Dewan Mushtaq Textile Mills Ltd
Textile Spinning
166
Dewan Textile Mills Ltd
Textile Spinning
167
Din Textile Mills Ltd
Textile Spinning
168
Ellcot Spinning Mills Ltd
Textile Spinning
169
Faisal Spinning Mills Ltd
Textile Spinning
170
Fatima Enterprises Ltd
Textile Spinning
171
Fawad Textile Mills Ltd
Textile Spinning
172
Fazal Cloth Mills Ltd
Textile Spinning
173
Fazal Textile Mills Ltd
Textile Spinning
174
Gadoon Textile Mills Ltd
Textile Spinning
175
Globe Textile (OE) Mills Ltd
Textile Spinning
176
Globe Textile Mills Ltd
Textile Spinning
177
Gulistan Spinning Mills Ltd
Textile Spinning
178
Gulistan Textile Mills Ltd
Textile Spinning
179
Gulshan Spinning Mills Ltd
Textile Spinning
180
Ideal Spinning Mills Ltd
Textile Spinning
181
Indus Dying and Manufacturing Co Ltd
Textile Spinning
182
Island Textile Mills Ltd
Textile Spinning
183
J.K. Spinning Mills Ltd
Textile Spinning
184
Maqbool Textile Mills Ltd
Textile Spinning
185
N.P. Spinning Mills Ltd
Textile Spinning
186
Nadeem Textile Mills Ltd
Textile Spinning
187
Nagina Cotton Mills Ltd
Textile Spinning
188
Paramount Spinning Mills Ltd
Textile Spinning
91
189
Premium Textile Mills Ltd
Textile Spinning
190
Reliance Cotton Spinning Mills Ltd
Textile Spinning
191
Saif Textile Mills Ltd
Textile Spinning
192
Salfi Textile Mills Ltd
Textile Spinning
193
Salman Noman Entreprises Ltd
Textile Spinning
194
Shadab Textile Mills Ltd
Textile Spinning
195
I.C.C. Textiles Ltd
Textile Weaving
196
Nakshbandi Industries Ltd
Textile Weaving
197
Prosperity Weaving Mills Ltd
Textile Weaving
198
Samin Textile Mills Ltd
Textile Weaving
199
Shahtaj Textile Ltd
Textile Weaving
200
Pak Datacom Ltd
201
202
Pakistan International Airlines
Corporation Ltd
Pakistan National Shipping Corp Ltd
Transport & Communication
203
Pakistan Telecommunication Ltd
Transport & Communication
204
Telecard Ltd
Transport & Communication
Transport & Comm.
92
Transport & Communication
93