The document presents a study on dynamic and sectoral analysis of working capital in Pakistan. It discusses literature on working capital management theories and previous studies. The study aims to investigate determinants of working capital across sectors in Pakistan using a sample of 79 non-financial firms from 2003-2014 in the textile and cement sectors. The research methodology section outlines the data sources, variables, and models to be used including descriptive analysis, correlation, OLS, and GMM. Key findings from the results and discussion section show significant relationships between working capital and variables like firm size, profitability and dynamism across sectors based on the regression models.

2. Dynamic and
Sectoral
Analysis of
Working
Capital. A
case of
Pakistan

3. Presentation Overview
• Introduction
• Literature Review
• Research Methodology
• Results and Discussion
• Conclusion

4. Introduction
• Working capital is most significant topic in corporate finance.
The concept of working capital (WC) was first developed by Karl
Marx.
• Guthman and Dougall (1948) noted that current assets less
current liabilities are known as WC and their analysis was
elaborated by Park and Gladson (1963).
• Working capital is considered an important part of short term
financial management. Long term financial management frequently
attains more consideration.
• Whereas most of the studies (e.g. Jose et al. 1996, Deloof 2003
and Rehman and Nasr 2007) have founded that short term financial
management has also a clear effects on company performance.

5. Introduction
• It is fact that the economic, financial growth for the firm, sector or
the country, the importance of sector involvement is considered the
strength of country economic growth (Kayo and Kimura, 2011).
• Naveed (2015), Kayu and Kimura (2011) tried to explain the factors
of sector is the important characteristic for evaluation measurement
of risk at sector level, because sector has exceptional risk factor,
challenges and other issues which can influence the WC
management variously. Therefore it need best solid base which is
necessary for financial growth of company.
• The dynamic model over the last few years has found strong support
in literature of working capital of developed and emerging markets
(Leary and Roberts, 2005, Flannery and Rangan, 2006).

6. Statement of Research Problem
The study mainly concentrated on firm and sector level factors as
determinants of Working capital.
• However, less attention has been given to sectors’ level behavior
which could influence the mechanism of firm and sector level
determinants on Working capital .
• A very little work has been done on the dynamic working capital
and its speed of adjustment have relatively less explored in
developing economies in the particular context of Pakistan
• the study thirdly highlights the literature gape in the purview of
dynamic Working Capital to examine whether Pakistani sectors have
target working capital and how speed of adjustment differs across
sectors.
• the study employs Generalized Method of Moment (GMM) to
recognize the existence of dynamic Working Capital across sectors
in developing markets, and looks into whether and how the speed of
adjustment varies across sectors.

7. Research Objectives
Research Objective 1. To investigate the significant determinants of working
capital across various sectors of Pakistan.
Research Objective 2. To find the distinctive nature of each sector influence
of working capital of firms across sectors.
Research Objective 3. To check whether and how different theories of
working capital pertinent to Pakistan economy.
Research Objective 4. To find the Pakistani firm’s across sector have dynamic
Working capital.
Research Objective 5. To examine the speed of adjustment varies across
different sectors in Pakistan.

8. Research Questions
1. What are the factors of working capital across various sectors
of Pakistan?
2. How distinctive nature of sectors and its impact of working
capital on firms across the sector?
3. Whether and how different working capital theories are
pertinent to Pakistani economy?
4. Does Pakistani firms across sectors have the dynamic WC?
5. Does the speed of adjustments differ across both sectors in
Pakistan?

9. Significance of the Study
• The study contributes in the development of working capital by
investigating the firm & sector effect on the working capital, further
significance of dynamic working capital and speed of adjustment across
sectors.
• The study will be useful for the researchers, investors of capital market and
management to provide a direction of investment decision.
• The financial analysts also get benefits from the study to make the
investment strategies for other investor of the market guiding and giving
ideas.
• Moreover where significance of WC is continually important in different
aspects. Therefore important thing is, thus how effectively firm’s are in
Pakistan preparation for short term financial needs even in various shocks in
worse economic conditions.
• The study provide practicable insights and facts that may help to determine
the speed of adjustment to help firms of Pakistan in their future investment
and growth decisions and to understand the dynamics of working capital.

10. Literature Review
• The effective working capital management extends appropriate availability
flow of funds to the firm and this improve growth opportunities for firms
increase return to shareholder (Smith, 1980). Therefore WC is essential for
entire business existence, sustainability and directly effect on firm performance
(Arnold, 2008, Gitman, 2009).
• Harris (2005) noted that WCM is considered a plain and easy notion of
ensuring the ability of company to finance difference between short term assets
and liabilities.
• Where it is further suggested and found to enhance sales opportunity shall
improve their inventory requirement which tend to increase deals in WC
(Wang, 2002).
• Chiou et al., (2006) demonstrated relationship for Taiwan firms and shared that
cash flow has positively impact over liquidity and negatively skewed over
working capital requirements. Moreover, they exposed that all those firms with
positive cash flows might better manage the requirements of working capital.
• Jiang and Lie (2016) maintained that firms having higher levels of cash
reserves have higher speed of adjustment than the firms facing cash deficiency.

11. Theoretical Background
Pecking Order Theory:
• Ross (1996) developed information asymmetry theory. The theory is as
well referred to as pecking order theory. Further it was proposed that new
investment are preferred by firms where retained earnings (internal source)
are used internally, then the debt and lastly to address equity.
Free Cash Flow Theory:
• According to Jensen and Michael, 1996), explain the causal effect
relationship between free cash flow and firm performance.
• Jenson (1986), corresponds on free cash flow hypothesis has suggestions
towards level of WC investment selected by the companies. In keeping
with, this management favors to boost current asset to add cash to maintain
flexible influence towards on company venture decision.
• This theory free cash flow has significant impact on working capital plan
and procedure of firm, Jensen’s (1986).

12. Hypotheses of the Study
Firm Level:
H1: Immediate growth opportunities have been positive relationship with WC
management of firms across sectors.
H2: Leverage has considerably negative impact over the WC management towards
firms across the sector.
H3: Operating cash flow fluctuations have considerably negative correlation with WC
of firm across the sector.
H4: Capital market access (size) has significantly positive association with WC of the
firms across the sector.
H5: The Profitability has positive relation with working capital of the firm towards at
across sector.
Sector Level:
H6: Munificence has various relationship with working capital of firm across the
sector.
H7: There is different relationship between dynamism and working capital investment
of firm across sectors.
H8: There is different relationship between HHI and working capital investment of a
firm across sectors.

13. Model of the Study
Firm level
Determinants of
WC (Lev, IGO,
Pro, FS, OCF)
Sector Level
Munificence,
Dynamisms &
HHI
Generalized
Method of
Moment
(GMM)
Working Capital
Textile Cement

14. Research Methodology
Research Design; This study tend to focused on quantitative research.
Sources of Data; The State Bank of Pakistan and Financial statement
analysis of different companies, World Bank development Indicators and
Federal Bureau of Statistics are the sources of data.
Duration of Data: This study sample population consists on non financial
listed firms in Pakistan exchange for the period of 12 years from 2003 to
2014. Two sectors Textile and Cement have been selected for the study.
Sample and Population: Total 79 firms is the sample and total population
of the study is 172 has been used for the study.
Panel Data: This study used Panel data that is combination of time series
and cross sectional.
Tools of Analysis: Descriptive, Correlation, OLS, Horsemen test, Fixed
effect, GMM model.
To differentiate between random and fixed effect model, Hausman test is
used. This study has to estimated the equation of GMM (Arellano and Bond,
1999, Naveed, 2015).

15. Variables measurement Proxies
Variables Measurements Evidence
1 Working
Capital
Current Asset – Current Liability / Total asset S.Bannas Cabalero.
etc. (2010)
2 Immediate
Growth Opp
The annual percentage change in total sales Hill etc al. (2010),
Chiou et. al. (2006)
3 Leverage Debt divided by equity Miller M.H (1977
4 Operating Cash
flow
Retained earnings + Depreciation – Taxes +/- Changes in
working capital
Appuhami (2008),
Petersen (1993)
5 Capital Market
Access (FS)
Natural log of total assets Moussawi et all.
(2006)
6 Profitability Return on Assets Padachi et al.(2006)
7 Munificence Taking the ratio of the regression slope coefficient to the
mean value of sales over the same period.
Naveed (2015), Kayo
and Kimura (2011),
8 Dynamism It is the standard error for munificence slope coefficient;
divided by mean value; of sales over the similar period.
Kayo and Kimuraa
(2001), Alon & Cuie
(2012),
9 HHI By summing the squares of percentage of market shares
held by the firms within a given sector.
Ghasemi, Nayebzadah
(2014)

16. Results and Discussion

17. Descriptive Statistics (Cement Sector)
Variables Mean Median Maximum Minimum Std. D
WC 1.098088 0.036731 0.356616 -2.400183 0.337729
FS 9.000685 9.060738 11.22292 6.080619 1.055355
LEV 2.902548 1.211000 175.8000 -8.2412 14.53610
OPCF 0.396488 0.444272 13.23803 -47.471 4.283762
PRO 0.054487 0.029000 0.376800 -0.3652 0.135007
IGopp 0.209604 0.146002 6.353089 -1.0123 0.623228
DAYN 0.381022 0.384407 0.505133 0.298827 0.064512
HHI 0.113181 0.111169 0.137179 0.099860 0.011825
MUNI 1.317057 1.267645 1.551554 1.196989 0.110606

18. Descriptive Statistics for (Textile Sector)
Variables Mean Median Maximum Minimum Std Dev.
WC 1.895978 1.375000 28.08000 -9.91 2.762648
DAYN 0.068551 0.027858 0.247957 0.020988 0.086399
MUNI 0.379609 0.292274 0.822848 0.231962 0.213977
HHI 0.025077 0.025727 0.028383 0.020988 0.002670
LEV 0.590823 0.233267 24.14111 -1 1.385279
FS 1.987318 0.737323 9.953753 0.653671 2.616686
OPCF 6.310367 7.284503 11.48297 -10.976 3.094699
PRO 0.551813 0.457814 18.21000 -38.85122 2.281376
IGopp 0.072990 0.038250 9.813000 -0.987 0.379433

19. Correlation Matrix for Cement Sector
Variable
s WC DAYN HHI LEV MUNI FS OPCF PRO SG
WC 1
DAYN 0.0136853 1
HHI 0.0092082 0.6730561 1
LEV -0.0638126 0.05075 0.0581664 1
MUNI 0.0081843 0.1708916 0.3865076 0.0561016 1
FS 0.4222877 0.3348922 0.3202543 -0.0856856 0.0694166 1
OPCF 0.0016897 0.0095111 -0.0056283 -0.0330513 -0.0643909 0.0351899 1
PRO 0.5867352 -0.1427737 -0.2236525 -0.1690331 -0.3442064 0.0704041 -0.0190367 1
SG -0.0947078 -0.0571708 -0.0940565 -0.0132813 -0.1257028 0.0076741 0.0108427 -0.0439021 1

20. Correlation Matrix for Textile Sector
Variables WC DAYN FS HHI LEV MUNI OPCF PRO SG
WC 1
DAYN -0.33296 1
FS -0.32598 0.679661 1
HHI 0.174386 -0.53209 -0.55208 1
LEV -0.19441 0.517006 0.517615 -0.31101 1
MUNI -0.33235 0.496961 0.673497 -0.47703 0.513982 1
OPCF 0.291419 -0.60464 -0.58808 0.525476 -0.47509 -0.69968 1
PRO 0.005134 -0.11073 -0.10781 0.020162 -0.05759 -0.11325 0.107996 1
SG -0.09537 0.182924 0.176045 -0.08709 0.079353 0.189392 -0.13412 -0.00552 1

21. OLS Model at Firm Level
Cement Sector Textile Sector Overall
IND Var. Coefficient P value coefficient P value Coefficient P value
C -1.29026 0.0001*** -0.16345 0.0001*** 2.328459 0.0123***
FS 0.124284 0.0023*** 0.023193 0.0145*** -0.27598 0.0001***
LEV 0.001497 0.2767 -0.00857 0.0006*** -0.00567 0.0251***
OPCF 0.000136 0.9763 -0.00327 0.2496 0.027808 0.4263
PRO 1.418783 0.0027*** 0.495362 0.0512** -0.03342 0.3752
IGOpp 0.03899 0.2173 -0.0023 0.7191 0.21965 0.2335
Adj R-Square 0.482685 0.007343 0.168079
Durbin-Watson stat 1.968688 1.867711 2.002652
F-Statistic 0.0001 0.031116 0.00000
Obs 800 148 948

22. OLS Model at Sector Level
Cement Sector Textile Sector Overall
IND Var. Coefficient P value Coefficient P value Coefficient P value
C 0.628276 0.3945 0.220078 0.6758 2.623007 0.0001
DAYN 8.411267 0.2301 -16.9594 0.0304** 4.54132 0.0531**
HHI -48.6846 0.2334 153.4274 0.1156 18.51514 0.0052***
MUNI 1.198536 0.241 0.666855 0.003*** -2.32883 0.0192***
Adj R-Square 0.23050 0.37043 0.18023
Durbin-
Watson stat 2.072126 1.867711 2.010296
F-Statistic 0.089046 0.009046 0.00010
Obs 900 148 948

23. D V Independent
variable
GMM (Overall) GMM (Textile) GMM (Cement)
WORKING
CAPITAL
MANAGEMENT
Variable Coefficient Prob Coefficient Prob Coefficient Prob
Lag of WCM 0.2894 0.0003 0.295212 0.0002 1.12669 0.6493
OCF -0.0004 0.705 -0.00554 0.0003 0.014482 0.9781
Immediate Growth
Opp. 0.0024 0.6693 0.007353 0.2464 0.045699 0.9815
Leverage -0.0062 0.0002 -0.00554 0.0003 -0.15354 0.8431
Profit 0.6318 0.0001 0.619715 0.0001 2.246655 0.9284
Capital Market access-0.0624 0.0001 -0.09353 0.0001 -0.12947 0.9593
Munificence 0.0917 0.1357 0.62274 0.0001 -15.694 0.8288
HHI -21.4141 0.0001 -49.5915 0.0182 617.7495 0.8975
Dynamism 4.2093 0.0002 10.03229 0.0001 -103.25 0.8687
Dynamic Working Capital

24. Cont….
Lag OCF -0.0017 0.6734 -0.00152 0.6734
Lag Immediate Growth Opp. 0.0029 0.605 0.034422 0.0001
Lag Leverage -0.0025 0.2079 -0.00382 0.0886
Lag Profit 0.1148 0.0004 0.1714 0.0001
Lag Capital Market access 0.0081 0.3448 -0.0287 0.0016
Lag Munificence -0.2629 0.0002 -0.28546 0.0002
Lag HHI 12.3132 0.0001 304.6953 0.0001
Lag Dynamism -1.2378 0.0065 -47.3169 0.0001
Lag Obs. 948 800 148
Sargan test 0.0968 0.22 0.98

25. Speed of Adjustment
Sector
GMM
Model
Speed of Adjustment
1-𝛌
Half-life
=Log(0.5) =Log (1-𝛌) =Log(0.5)/
Log(1-𝛌)
Over-all
Sample
GMM 1 0.70 -0.3110 -0.1466 2.12 Years
Textile 0.69 -0.3210 -0.1326 2.42 years
Cement Static -0.3011 0

26. Conclusion

27. Significant Determinants of WC
Firm Level Results Evidence
Firm Size (+) In line with firm level the firm size
is positive significant in overall
findings. Firm size maintains
positively correlated with working
capital.
Moussawi et al 2006, Chiou et al,
2006, Niskanen, 2006, Banos
Caballero et al Hill et al, 2010)
who suggested large firms have
positive impact on WC.
Profitability (+),
Immediate
Growth Opp (+)
profitability and immediate
growth opportunity are also
positively significant with WC.
In support of study (Shawn, 2015,
S.Banas Caballero, 2014, Myers,
1984, Aktas, 2015, have also
supported our results.
Operating
Cash flow (-)
In contrast operating cash flow is
negatively significant with
working capital.
S.F.Mun and S.Jang, 2015,
Wasiuzzaman and
Arumugam,2013.have also
supported our results.
Munificence
(+),
HHI (+)
Finally at sector level two variables re significant like munificence and
HHI. The sign of coefficient for both variables are positive and its value
is significant. The persistent growth of firm is called Munificence and
these lead firms have high rate of Munificence have greater level of
opportunities as compare to low level. (Zafar iqbal & Munawar
Hussain, 2017, Almazan and Molina (2005), Dess and Beard (1984).

28. Summary of determinants of Working Capital differ
across the sectors.
Overall Textile Sector Cement Sector
Firm Size +*** + +***
LEV + + +***
Prof +** + +***
IGO -** +** +**
MUNIF + *** +*** -***
DAYN +*** - *** +***

29. Pertinence of Working capital theory across sector of
Pakistan.
Sectors Variables Theories
Textile Sector Leverage (-) Pecking Order Theory
Dynamism (-) Pecking Order Theory
HHI (-) Pecking Order Theory
Cement Sector Operating Cash flow
(+)
Free Cash flow Theory
Leverage (-) Pecking Order Theory

30. Speed of adjustment at across the sector of Pakistan
Sectors Speed of Adjustment
Overall 2.12
Textile 2.42
Cement No speed of
adjustment exist
Static in nature

31. Implications of the Study
• One of the important implications for listed firms is recognize the
sector specific effect over the working capital management. This study
also provide guidelines to banks in setting the benchmark exposure for
certain sectors/industries.
• Furthermore the variation of working capital determinants across
sectors also highlight the important factors which could have direct or
indirect effect over the working capital management.
• Another important practical implications for the firms, is the
recognition of dynamic or static nature of working capital of various
sectors. This could help the banks in provision of running finance for
working capital management

32. Limitations
• Selected companies are Non-financial.
Financial companies are not a part of this
study.
• Some other techniques can be used for
determining the presence of this relationship.
• The study used data of 79 firms from two
sectors from 2002 to 2014. Both the size and
time period in this case can be extended.

33. Future Research Directions
• In line with the study remained focused on non financial
sectors of Pakistan One could use determinants for working
capital other then firm and sector level such as country level
factors and add some other variables to check the impact.
• To investigate by using dynamic modeling with current
data e.g. 2015-19 and especially in financial sector this study
represents an outline for future research.
• Further with the availability of large scale data the impact
of macroeconomic factors on financial sector can be examine
as well.
• The study is across the sector it can be extend to across
the country as well.