1
“Final Project Report”
Topic
:
Group Members
:
Haiqa Malik (2019-BBA-027)
Javeria Asif (2019-BBA-033)
Muqadas Qayyum (2019-BBA-054)
Dua E Zahra (2019-BBA-)
Subject
: Corporate Finance
Department
: Business Administration
Semester
:
Date of Submission
:
VIII
21/06/2023
2
Abstract
This study aims to investigate the impact of working capital policy on firm performance in the
context of the cement industry in Pakistan, moderated by firm size and controlled for liquidity and
sales growth. We collected the required data of a total of 20 Pakistani cement firms for running
multiple software tests which helped us identify the framework relationship. The regression
analysis suggests that the selected independent variables have a significant impact on the firm
performance as measured by ROA and ROE. The findings of this study will offer insight to
organizations regarding the importance of designing and implementing effective working policies
that would impact their financial performance in the long run, ultimately leading to success and
growth. Based on our findings it is recommend that firms should develop industry specificguidelines to determine the best way to manage their working capital.
3
Table of Contents
1. Chapter 1. Introduction ....................................................................................................4
1.1
1.2
1.3
1.4
Background of study ………………………………………………………………..4
Problem Statement …………………………………………………………...……..5
Objectives of study ………………………………………………...……………….5
Significance Of study ……………………………………….……………………...5
2. Chapter 2. Literature Review ………………………………………………..…………6
3. Chapter 3. Methodology ..................................................................................................7
3.1
3.2
3.3
3.4
3.5
3.6
3.7
Research Design …………………………………………………………………….9
Sample Size …………………………………………………………………………9
Research Instrument …………………………………………………………….….9
Software Used ……………………………………………………………………...9
Theoretical Framework …………………………………………………………….9
Hypothesis………………………………………………………………………….10
Variable Measurement ……………………………………………………………10
4. Chapter 4. Results ……………………………………………………………………10
5. Chapter 5. Conclusion ……………………………………………………………....11
6. Chapter 6. Future Implications …………………………………………………….12
7. Chapter 7. Limitations ………………………………………………………………13
8. References
4
Chapter No.1: Introduction
Efficient management of an organization’s resources is essential for the success of any
organizations. In today’s business world, organizations face an immense amount of pressure
regarding WCM. Working capital means “the investment used to meet the firm’s day to day
operations”. The way in which organizations design and implement their policies for working
capital has a major impact on the firm’s profit and liquidity. Effective and efficient WCM deals
with the management of CA and CL of a firm in a way that would help them to eliminate the risk
of any sort of incapacity to meet short term liabilities along with avoiding excessive investment.
WCP consists of two parts, first one is WCI policy which focuses more investment on the level of
CA and second is WC. Financial Policy which involves financing of firms assets through short
term liabilities. A firm should create balance between its profitability and its day to day operations,
while insufficient amount of WC lessen a firm’s liquidity and holding of excess WC results in the
depletion of profitability of a firm (Ghosh & Maji, 2003).
Size is observed as a major determinant of firm’s performance, this has even been explained
theoretically by the economies of scale. There are a few studies on this, in one study it was found
that there is a negative relationship between firm size and firm FP (Kouser et al. 2012). Baumol
(1959) also coincide that large firm size can lead to inefficiencies and lower profits.
With WC being one of the most crucial functions of corporate management, in this research we
have also linked the concept to liquidity and sales growth, having them act as controlling variables.
(Smith, 1980) The management of these short term assets and liabilities demands careful
allegiance as Management of WC plays a important role in the calculation of a firm’s profitability,
liquidity , risk and also helps in achievement of goal of avalue. As well as the objective of
achieving value. (Jeng-Ren et al. 2006) .Due to influence on firm’s profitability and liquidity
WCM is considered important for corporate finance, proper management can lead to success
whereas poor management can lead to bankruptcy (Padachi et al.2008).
Background of Study:
There have been various studies conducted to find and know about relationship between WCM
and profitability ratio of firm. (Filbeck and Krueger, 2005) conducted a study to find the
significance of the management of WC. Liquidity and profitability being a major competent in
WCM was studied by Kargar and Blumenthal in 1994.
The cement industry as a significant segment of the industrial sector plays a important role in the
economic development of Pakistan.There are a total of 20 other industries that are interconnected
with cement and construction sector. The cement sector is vibrant and is growing, having the
production capacity of 20 million tons, and about 11 million tons cement exported from Pakistan
which contributes to the economy. Pakistan is ranked 5th in cement exports. In this research we
5
collected the required data of a total of 20 Pakistani cement firms for running multiple software
tests which would help us identify the framework relationship.
Problem Statement:
Despite WC policy playing a significant role in shaping the firm’s performance, there has been
limited research conducted on this relationship being moderated by the moderator if firm size.
“Does WCM Policy being moderated by firm size, affect the profitability of the cement industry
in Pakistan?”
Objectives of study:
Main objectives of our study are following:
 To identify the impact of WC policy on firm FP , in the cement sector of Pakistan.
 To see identify the effect of WCM on firm performance of cement sector with the
moderating role of firm size.
 To see the controlling impact of liquidity and sales growth on the WC policy and firm
performance, in the cement industry of Pakistan.
Significance of Study:
In this research, we investigate the impact of WC policy on a firm’s financial performance,
moderated by firm size in a Pakistani context. Previous studies have either showed a positive or
negative relationship between the independent and dependent variable, the moderating effect of
firm size has not been well understood. For this purpose, this study will provide empirical evidence
on this relationship along with filling the gap of how firm size can act as a moderator between
WCM and firm performance. From a practical standpoint, the findings and results of this study
will offer insights for organizations regarding the importance of designing and implementing
effective working policies which would positively impact the financial performance.
6
Chapter No. 2: Literature Review
Underlying Theory;
The Underlying theory of our research is ‘WCM Theory”. The theory aims is efficient use of by
monitoring and optimizing the use of its Current Assets and Current Liabilities.
The Second Theory which we considered in our Research is “A Resource base view of a firm”
which means anything which could be consideration of a strength or weakness of a given time.
Resource at a given time could be defined as these assets which are semi permanently to the firm
(e.g. brand name, in house knowledge of technology, employment of skilled personnel, trade
contract, capital, machinery, and efficient product).
Working Capital Policy (WCP):
Firm’s WC Policy means their level of investment in their current assets to achieve their
goals.A lot of Research Specialists have studied the working Capital policy has been studied by a
lot of research specialists who have given different views. For instance, in a study Aktas et al.,
(2015); Deloof, (2005); Yazdanfar & Ohman (2014). It is said that proper WCM has a major effect
on the financial performance of a firm. WC is internal fund resource which is used to meets the
firm’s current liabilities as it provides liquidity to firms. Another study (Panda & Nanda, 2018)
has shown that if a firm were to hold more working capital then it could cause a high cost of
liquidity and vice versa. The appropriate management of working capital is essential to ensure that
a firm maintains adequate liquidity while maximizing profitability (Huang, Lu, & Zhang, 2019).
Hill and Hsin (2013) stated the importance of a company’s working capital being in line with the
industry and growth rates of the company, where an aggressive working capital management
policy positively impacts profitability. Duc et al. (2019) found that efficient management of
inventory and accounts receivable increases profitability and lower financial risk, Singh and
Kumar (2017) on the other hand argue that effective WCM can improve the profitability and
liquidity ratio of a firm. (Lazaridis & Tryfonidis, 2006) investigated the WCM effect on the
profitability ratio of Greek firms.This study shows that there is a negative effect of aggressive WC
policy on firm’s profitability.
Financial Performance:
A study by (Morara & Sibindi, 2021) states that an appropriate use of a firm’s assets along
with keeping a firm’s rivals and completion in mind is what shows how well the firm is performing.
(Kaushik & Chauhan, 2019) conducted a study to examine the WC policy relationship with firm
7
financial performance for which they selected 211 firms in India and firm’s data from 2008 to
2016 was taken for study. (Mabandla, 2018) documented a solid association between firm financial
performance and aggressive WCP., a study by Pestonji and Wichitsathian (2019) found highly
significant relationship of Working Capital Investment Policy on Firm Financial Performance. A
study by (Benrquia and Jabbouri, 2021), shows that the monitoring of WC is crucial to assure the
abundant liquidity level and to ensure accurate daily operations while also enhancing the
performance and value of the firm. Akoto et al. (2013) also studied this by collecting data of
different Ghanaian companies, and the results proved that WCM positively effects firm’s financial
performance. Rehman et al. (2010)’s analysis of 204 Pakistani manufacturing firms revealed the
net sales, conversion cycle of cash and the firm’s inventory turnover having a notable impact on
performance of the firm.
Firm Size:
“Size-Performance” theory (Demsetz & Lehn, 1985) suggests that there is a positive
relationship between the firm size and its FP , with larger firms tending to generate more revenue,
profit and higher market value compared to smaller firms .A study by Vakilifard and Taslimi
(2021) found that larger firms tend to have better FP than smaller ones due to the economies of
scale they enjoy. Similarly, Karabag and Kilincarsan (2021) discovered that larger firms have a
higher ROA and equity as compared to small firms. However, the “Resource-Based” theory
(Barney, 1991) offers a different perspective and argues that the effect of firm size on profitability
is not always positive because profitability also depends on resources firm have. A study by
Ljubownikow and Servali (2020) suggests that larger firms tend to have lower return on assets
than smaller ones because of their difficulty in adapting to changing market conditions. There are
some scholars who have found that there is no impact of firm size on firm financial performance.
For instance, Study by Arora and Sharma (2019) revealed that in different sector industries there
is no consistent link of firm size on firm financial performance.
Relationship of WCM and FP:
Many studies have proved that WCM and FP have positive relationship with each other.Several
studies have shown that companies which uses efficient WCM have better FP.For instance, Deloof
(2003) found that Belgian firms with efficient WCM had higher profitability than their peers.
Similarly, Lazaridis and Tryfonidis (2014) through his study proved that there is positive impact
of effective WCM on FP of firm of Greek companies. Kim and Sorensen (1986) highlighted the
negative impact of excessive WC on profitability in a sample of small Korean firms. Similarly,
Eljelly (2004) found that WCM have a negative relationship with firm FP in Saudi Arabia firms.
Overall, these studies indicate that WCM plays a crucial role in determining the FP of companies.
Therefore, companies should focus on efficient management of their WC to enhance their
profitability and financial stability.
8
Liquidity relationship with Firm Financial Performance:
Scholars have different views on the liquidity effect on firm financial performance. For example,
Dichev and Piotroski (2001) found a positive impact of liquidity on firm financial performance in
US companies. Whereas, some scholars argue that high liquidity might not necessarily lead to
better financial performance. For instance, Abor (2005) found no significant relationship between
liquidity and profitability for firms in Ghana. Moreover, some scholars suggest that the relationship
between liquidity and FP might be nonlinear or context-dependent. For example, Ng and Smith
(2019) found that low liquidity was associated with lower FP in the short-term, while high liquidity
had a negative impact in the long-term for Australian firms. Overall, liquidity effect on FP is not
straightforward and can vary depending on various factors, such as the industry, firm size, and
economic conditions.
Sales Growth and Financial Performance:
Many Scholars have studied the relationship of sales growth with Financial Performance of Firm.
Some researchers suggest that high sales growth leads to higher profitability and shareholder
wealth. For example, Li and Faff (2011) found that sales growth positively affect firm financial
performance. However, other studies suggest that high sales growth can lead to lower profitability
due to increased costs associated with growth. For instance, Scherreiks and Steensma (2018) found
that there is a negative relationship between sales growth and profitability for Dutch retail firms.
Moreover, some scholars suggest that the relationship between sales growth and FP is not linear
and can vary depending on the industry and firm size. For example, Kavalec and Charoenwong
(2018) found a curvilinear relationship of sales growth on profitability for firms in the healthcare
industry in Thailand. There are also studies that suggest no significant relationship between sales
growth and financial performance. For instance, Kim and Heo (2018) found no positive effect of
sales growth on firm performance for Korean firms.
9
Chapter No 3: RESERCH METHODOLOGY:
Research Design:
Our research was based on longitudinal data as all the data information was publically available
and is categorized as time series data of five years (2018 to 2022). The data was extracted from
the annual financial reports of the companies which we have selected and the ratios are calculated
in order to gain comparability among the companies. We have taken the data of the companies of
cement industry of Pakistan. The study is conduct in order to draw a conclusion of relationship
between Working Capital Policy (Independent Variable) and firm profitability (Dependent
Variable).
Sample Size:
We have taken total 20 cross section companies from the cement industry of Pakistan to collect
data and the total number of observations are 100.
Research Instrument:
The instrument that we have used to collect data for our research is the annual report of the
companies. . The annual financial reports contains all the data which we needed for the calculation
of the ratios which we needed in our study.
Software Used:
The software used in our research is first of all Microsoft Excel. We have entered all the data
extracted from annual financial reports into Microsoft excel and we have calculated ratios needed.
Secondly we used E-view to get the results of our plan Analysis.
Theoretical Framework:
10
Hypothesis:
H1: There is positive relationship between IV and DV.
H2: Moderating Variable moderates the relationship of IV with DV.
VARIABLE MEASURMENT:
Working Capital Policy Decision:
The Independent Variable in our study consist of IP and FP. In the Article “Impact of WCP on
Firm’s Profitability: A Case of Pakistan Cement Industry” the measures of WCP is IP and FP.
Independent Variable:
Variable
Measures
Abbreviation
Investment Policy
Total Current Assets/Total
IP
Assets
Financing Policy
Total Current Liabilities/Total
FP
Assets
Financial Performance of Firm:
In Article “Impact of Financing decisions ratios on firm accounting-based performance: evidence
from Jordan listed companies” following measures are used to measure the Independent Variable.
Dependent Variable:
Variable
Measures
Source
Return on Assets (ROA)
Net profit / Total assets
Aphagia and Gavoury (2011)
Return on Equity (ROE)
Net Income / Average Total
Odusanya et al. (2018)
Equity
Net Operating Profit
(Earnings before Interest and Nill
Tax + Depreciation) / Total
Assets
Firm Size:
We have taken a firm size as a moderator in our study to check the impact of firm size on the
working capital policy and firm performance.
11
Moderating Variables:
Variable
Measures
Source
Firm Size
Natural logarithm of turnover
Ghayas and Akhter (2018)
Liquidity and Sales Growth:
In the line with previous studies, in addition to our IV, Liquidity and Sales Growth are included as
control variables in our study to control a set of firm specific observable characteristics that are
likely to be correlated with firm performance.
Controlling Variables:
Variables
Liquidity
Sales Growth
Measure
Source
Current Ratio = Current Liabilities Current Great Lakes Herald
Assets
(2010)
Natural Log Sales of current year / Sales of
Previous Year
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Chapter No. 4: Results
SUMMARY STATISIC:
Table 4.1: Descriptive Statistics:
Variables N
Mean
Std. Dev.
Min
Max
FP
100
0.478900
0.813409
0.010000
6.420000
FS
100
6.931400
0.659260
5.390000
8.520000
IP
100
0.348400
0.262385
0.070000
1.070000
LIQ
100
2.158700
4.757987
0.160000
30.81000
NOP
100
0.112000
0.170217
-0.55
1.020000
ROA
100
2.216200
4.693427
-15.56
13.70000
ROE
100
3.700400
8.301506
-37.48
20.42000
N=No. of Observations Std. Dev. = Standard Deviation, Min= Minimum, Max= Maximum
Sources= the data is collected from the annual statements of the companies. The abbreviations
used for FP: Financing Policy, FS: Firm Size, IP: Investment Policy, LIQ: Liquidity, NOP: Net
Operating Profit, ROA: Return on Assets, ROE: Return on Equity.
Table 4.1 of this report is depicting the results of descriptive statistics for Mean, SD, min and max
values of the table. The Financing Policy the companies we have taken ranges from 0.010 to 6.420
with a mean of 0.478900 and a standard deviation of 0.813409 which shows
Table 4.2: Correlation Analysis:
Variables
FP
FS
IP
LQ
NOP
ROA
ROE
FP
1.000000
FS
-0.107664
1.000000
IP
-0.171037
-0.195438
1.000000
LQ
-0.159122
-0.434643
0.514003
1.000000
NOP
-0.118404
0.293084
-0.029894
-0.113599
1.000000
ROA
-0.169387
0.327785
-0.057108
-0.038156
0.218443
1.000000
ROE
-0.156542
0.368117
-0.077106
-0.047785
0.192739
0.960605
1.000000
SG
0.499686
0.046269
-0.233463
-0.007781
-0.185007
0.040090
0.049253
SG
Table 4.2 shows a correlation analysis of all the variables. When the value of correlation is 1, it
exhibits a very perfect strong and positive correlation on the other hand, if the correlation is -1,
1.000000
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shows a perfect negative correlation. However, the 0 correlation indicates there is no relationship
exist between the variables.
In the case of FP (Independent Variable) , it has a perfect correlation with itself, It has a positive
correlation with SG indicating that increase in Sales Growth will have positive impacts on its
Working Capital Policy. FS, IP, LQ, NOP, ROA AND ROE are negatively correlated with FS.
There is a negative Correlation between FS (Moderator) and FP, IP and LQ depicts that increase
in firms Sizes will have negative impact on its Liquidity and Investing Policy. NOP, ROA,ROE
AND SG are positively correlated with FS.
IP (Independent Variable) is positively and significantly correlated with liquidity of firm, which
indicates that increase in Liquidity will have positive impact on investing policy of firm. Whereas
NOP,ROA,ROE and SG have negative correlation with IP.
LQ is negatively correlated with all the variables except IP.
NOP is positively correlated with FS, ROA and ROE indicates that increase in NOP will have
positive impact on Firm Performance. NOP is negatively correlated with rest of the variables.
ROA is positively correlated with FS, NOP, ROE and SG shows that Increase in ROA will leads
firms towards the better Financial Performance. The variable is negatively correlated with FS, IP
and LQ.
ROE is negatively correlated with FP, IP and LQ shows that Working Capital policy and Liquidity
policy of the firm with negatively impacts the Firms performance. Rest of the variables are
positively correlated
SG is negatively Correlated with IP, LQ and
HAUSMAN TEST SPECIFICATION:
The results of the Housman test depict that the regression estimates derived from fixed effects
models are more appropriate for the analysis because they are more consistent and efficient. The
test failed to meet the assumption of the random effect model in which prob > 0.05.
REGRESSION ANALYSIS:
Dependent Variable: ROA
Cross Sections Included: 20
Total Panel (balanced) Observations: 80
Table 4.3: Panel Regression Analysis (ROA)
14
Coefficient
Standard
Prob.
(B)
Error
0.428612
4.232665
0.9197
C
0.160086
10.4813
0.9879
IP
1.215296
3.704917
0.7441
FP
0.06129
0.146266
0.6768
LQ
3.1809
0.939332
0.0013
SG
-0.011083
0.126631
0.9306
ROA (-1)
0.533016
R-squared
Adjusted R-squared 0.329241
Durbin-Watson stat 2.783046
2.61571
F-static
0.001662
prob. (F-statics)
In our Paper regression Analysis is used to measure the firm performance during the period 2018
to 2023, to measure the firm performance we have used ROA,ROE and NOP as dependent
variable, IP, FP, LQ and SG of the firms as Independent and Controlling Variables Respectively.
Variables
In the evaluation of Table 4.3, where the results of regression analysis regarding the impact of IV
on DV are demonstrated.
R-Square also known as the coefficient of determination shows that how much change and
variation in dependent variable is caused by independent variables. The value of R-square
0.533016, which indicates that the independent variable are responsible for 53.30% variation in
the dependent variable which is ROA. It also means that the variables we have taken to find out
the relationship are highly effected and the remaining 46.70% constitute those variables or factors
that we have not taken. The value of R-square is strong and Fit in our model.
The adjusted R-square is a modified version of R-square which tells whether adding or removing
additional Variables improves a regression modal or not. The value of Adjusted R-square in the
table is 0.329241 in the table.
In order to find the autocorrelation in the regression analysis Durbin Watson’s value was
computed. The value of Durbin Watson Stat is 2.783046 which is greater than 2 which shows that
there exist no autocorrelation but a negative correlation between ROA and independent variables.
The F test explains of the relationship between a dependent variable and all independent variables.
The F-statistics is used to test the significance of R, from the results of our table F-statics is
2.61571which is insignificant.
The overall significance of the table can be interpreted by the value of the Probability of F-statics.
If the probability value of F-static is less than 0.05 the model will be significant. The p-value of
F-statics is 0.001662 in our modal which is less than 0.05 which shows that p-value of the F-static
is significant.
15
Dependent Variable: ROE
Cross Sections Included: 20
Total Panel (balanced) Observations: 80
Table 4.3: Panel Regression Analysis (ROA)
Variables
Coefficient (B)
Standard
Error
Prob
PC
2.539385
7.986575
0.7517
IP
-6.435287
19.82258
0.7467
FP
3.56572
7.040917
0.6146
LQ
0.089101
0.278388
0.7501
SG
6.263719
1.785066
0.0009
ROE (-1)
-0.069435
0.150712
0.6468
0.518957
R-squared
Adjusted
R0.309047
squared
Durbin-Watson
2.495777
stat
2.472282
F-static
0.002873
prob. (F-statics)
Table 4.3 depicts the results of the regression analysis of Dependent Variable ROE (Return of
Equity) along with independent Variables.
The Value of R-Square in the table is 0.518957, which shows that the independent variables of the
study are responsible for 51.89% of variation out of 100% in the dependent Variable which is
ROE. Which means that remaining 48.11% consist of those Independent variables which are not
taken in study. The value of Adjusted R-square is 0.309047 which is 30.90 in the table which is
less than R-square. The value of Durbin-Watson test is 2,495777 which is greater than 2 which
shows that there is a negative correlation exist. The value of Prob of F-statistic in a table is
0.002873 which seems to be less than 0.5 so it is significant.
Dependent Variable: NOP
Cross Sections Included: 20
Total Panel (balanced) Observations: 80
Table 4.3: Panel Regression Analysis (NOP)
16
Variables
C
IP
FP
LQ
SG
NOP (-1)
R-squared
Adjusted R-squared
Durbin-Watson stat
F-static
prob. (F-statics)
Coefficient
(B)
0.345118
0.10228
-0.50011
0.000649
0.043681
-0.518823
0.692832
0.558795
2.134259
5.16897
0
Standard
Error
0.119894
0.297884
0.109066
0.004256
0.028523
0.075782
Prob.
0.0057
0.7326
0
0.8794
0.1314
0
Table 4.3 shows the results of the Panel Regression Analysis of Dependent Variable which is NOP
along with other independent and controlling Variables.
As, we can see in the table that the value of R-square is 0.692832 which shows that independent
variables are responsible for 69.28% variation in dependent variable which is NOP(Net operating
Profit). The remaining 31.72% consist of those independent variables which are not taken in
studies. The value of Adjusted R squared is 0.558795 which is 55.87%. The value of DurbinWatson test is 2.134259 which is greater than 2, which indicates that the negative correlation exist.
The value of Prob. (F-statics) is 0 which is less than 0.05 that mean it is significant.
Dependent Variable: ROA
Cross Sections Included: 20
Total Panel (balanced) Observations: 80
Table 4.3: Moderated Regression Results:
Variables
C
IP
FP
LQ
SG
ROA(-1)
FS
FS*IP
Coefficient
(B)
-79.41603
-39.10687
25.97508
14.43834
-0.342103
-0.514643
11.40126
9.631354
Standard
36.45361
92.98596
20.50544
4.206163
1.563792
0.160532
5.160371
13.20331
prob.
0.0341
0.6759
0.2111
0.0012
0.8277
0.0023
0.0318
0.4691
17
FS*FP
FS*LQ
FS*SG
R-squared
Adjusted R-squared
Durbin-Watson stat
F-statistic
Prob (F-statistic)
-4.69222
-2.524711
0.128034
0.605739
0.377068
2.497448
2.648952
0.00121
2.982539
0.722025
0.252767
0.122
0.001
0.6147
From the table 4.3, reveals that there is a positive relationship between IP and ROA when
multiplied by the moderator FS (Firm Size). The coefficients of FP and LQ showed a negative
relationship with Depended variable ROA when multiplied by moderator FS, while SG shows a
positive relationship with ROA by multiplied by the moderator FS.
The value of R square in the table is 0.605739 which reveals that independent variables are
responsible for 60.57% variation in the Dependent Variable ROA and 39.34% other factors are
responsible which are not taken. The value of the Adjusted R-square is 0.377068 which is 37.70%.
The value of the Durbin-Watson stat is 2.497448 which
Dependent Variable: NOP
Cross Sections Included: 20
Total Panel (balanced) Observations: 80
Table 4.3: Moderated Regression Results:
Variables
C
IP
FP
LQ
SG
NOP(-1)
FS
FS*IP
FS*FP
FS*LQ
FS*SG
R-squared
Coefficient
Standard
prob.
(B)
-0.514197
0.945907 0.5891
-1.343166
2.444479 0.5851
-0.290954
0.539387
0.592
0.048683
0.111806 0.6651
0.0355
0.041849 0.4003
-0.561539
0.07612
0
0.129812
0.133394 0.3352
0.198334
0.346365 0.5695
-0.037497
0.07924 0.6381
-0.006424
0.019191 0.7392
-0.006018
0.006764 0.3778
0.73397
18
Adjusted R-squared
Durbin-Watson stat
F-statistic
Prob (F-statistic)
0.579673
2.106194
4.756858
0.000001
The above table shows that the interaction of moderator firm size and IP has a positive impact on
the dependent variable NOP, which clearly shows that an increase in firm size and the resultant
increase in IP leads to an increase in NOP and vice versa which shows a direct relationship among
them. However the relationship between is non-significant (0.5695 is greater than 0.05).
The interaction between FS into FP is negative (-0.0374), this shows an inverse and negative
impact on the dependent variable NOP. This means than an increase of one unit in FS and FP that
would lead to a decrease in one unit of NOP. The relationship is non-significant because the
probability is 0.6381 which is greater than 0.05
The interaction between FS into LQ is negative (-0.0064), this shows an inverse and negative
impact on the dependent variable NOP. This means than an increase of one unit in FS and LQ that
would lead to a decrease in one unit of NOP. The relationship is non-significant because the
probability is 0.7392 which is greater than 0.05
The interaction between FS into SG is negative (-0.0060), this shows an inverse and negative
impact on the dependent variable NOP. This means than an increase of one unit in FS and SG that
would lead to a decrease in one unit of NOP. The relationship is non-significant because the
probability is 0.3778 which is greater than 0.05
The value of R square which is 73% shows that all the interacting variables cause a 73% variation
on the dependent variable. The adjusted r square value shows it all the interacting variables cause
a 57% variation on the dependent. The durbin Watson value which is 2.10 (less than 2.5), this
shows that auto correlation does not exist.
The coefficient value of IP is -1.343, one unit increase in IP would lead a 1.3 unit decrease in NOP.
The coefficient value of IP is -0.290, one unit increase in FP would lead a 0.29 unit decrease in
NOP. The coefficient value of IP is 0.048, one unit increase in LQ would lead a 0.048 unit increase
in NOP. The coefficient value of IP is 0.0355, one unit increase in SG would lead a 0.0355 unit
increase in NOP.
The probability value is 0.000001, which is less than 0.05, this shows a significant relation.
19
Dependent Variable: NOP
Cross Sections Included: 20
Total Panel (balanced) Observations: 80
Table 4.3: Moderated Regression Results:
Variables
C
IP
FP
LQ
SG
ROA(-1)
FS
FS*IP
FS*FP
FS*LQ
FS*SG
R-squared
Adjusted
Rsquared
Durbin-Watson
stat
F-statistic
Prob (F-statistic)
Coefficient
(B)
-64.61323
-253.2767
60.44337
2.688336
1.094413
-0.2735
10.57568
33.15471
-8.931738
-0.179373
0.145515
0.63054
0.416253
2.525335
2.942505
0.000392
Standard
65.21567
151.4168
38.25771
21.15454
7.088905
0.157134
9.288526
21.46236
5.640237
3.154599
1.211913
prob.
0.3266
0.1006
0.1204
0.8994
0.8779
0.0879
0.2603
0.1287
0.1196
0.9549
0.9049
20
Hypothesis Testing:
H1: There is a positive relationship between IV and DV.
According to the Regression panel analysis of the dependent variables ROA, ROE and NOP. The
results show a significant relationship. From this we can conclude that there is a positive
relationship between the independent variable working capital policy and dependent variable firm
financial performance. Thus H1 is accepted.
H2: Firm size moderates the relationship between IV and DV.
According to the moderated regression analysis of the dependent variables ROA, ROE and NOP.
The results show a significant relationship. Thus H2 is accepted.
Chapter No. 5: Conclusion
The WCM effective way is critical for the success of any organization. This study was conducted
with a aim to investigate the impact of WCP on FP in the context of the cement industry in
Pakistan, moderated by firm size, and controlled for liquidity and sales growth. The previous
literature suggests that WCP has a significant impact on a firm's profitability and liquidity, and
firm size plays a moderating role in this relationship.
The Study’s Methodology includes Hausman test and fixed-effect regression models. According
to results, the regression analysis suggests that the selected independent variables have a
significant impact on the firm performance as measured by ROA and ROE. However, the impact
varies based on the moderator effect of firm size. It is important to note that there may be other
unobserved factors that may influence firm performance, And the results of the correlation analysis
suggest that the Financing Policy, Firm Size, Investment Policy, Liquidity, Net Operating Profit,
Return on Assets and Return on Equity are important variables that are significantly correlated
with each other and may have significant impacts on the financial performance of the firms.
The study aims to provide empirical evidence on this relationship, filling the gap in the existing
literature. The findings of this study will offer insights to organizations on the importance of
designing and implementing effective working capital policies that would positively impact their
financial performance in the long run, ultimately leading to success and growth.
21
Future Implications
The working capital policy impact on firm financial performance moderated by firm size and firm
age is a complex and complicated issue with many variables at play. Further research and analysis
are needed to fully determine the impact of WCP on firm FP and to identify the best policy for
different firm sizes and ages. Below are some future implementations:
1. One possible direction for future research is to analyze how a company's working capital policy
affects its financial performance, taking into account industry-specific factors. Different industries
have different capital needs and face different competitive factors, which may affect the best
working capital policy. Identifying these factors can provide useful information for businesses in
designing their policies to improve their financial performance.
2. Another possible direction for future research that is WC by looking at how different companies
in the same industry handle their finances. By comparing these practices, it can show which
methods lead to better financial performance and efficiency. This comparative analysis can help
to identify effective practices in working capital management that helps for higher financial
performance and efficiency, and provide targets for firms to evaluate their policies
3. Many f the existing research focuses on the short-term impact of WCP on firm FP. Future
research can analyze the long-term impact of WCP on firm FP moderated by firm size and firm
age. Such empirical analysis is necessary to understand whether the short-term benefits of a
specific working capital policy balance the long-term drawbacks.
22
Chapter No. 6: Recommendations
1. Based on our findings, it is recommended that firms should develop industry-specific guidelines
to determine the best way to manage their working capital. This will help them understand how
different factors affect their working capital policy.
2. We recommend that future research should investigate the long-term impact of WCP on firm
FP moderated by firm size and firm age. This long-term analysis will provide insights into whether
the short-term benefits of a specific WCP outweigh the long-term drawbacks
3-Firms should use certain metrics to measure how well their working capital policy is working.
This will help them compare their performance to other companies in the same industry.
4. To improve their management of working capital, firms can use financial technology (FinTech)
tools to manage their cash flow, inventory, and other financial information.