The Impact of the Textile Sector on the Pakistan Stock Exchange
TALHA SHAKEEL, HAMZA QAYYUM, SHAYAN FAZAL, SALEHA JAVED KAYANI,
DR. DANISH HASAN, USAMA RAZA
Department of Textile and Clothing
National Textile University Karachi Campus
St-2/1, Sector 30 Korangi Industrial Area, Karachi, Sindh 74900
PAKISTAN
Abstract: - This research paper aimed to examine what impact does the textile industry has on the Pakistan
Stock Exchange (PSX). In Pakistan’s economy, the textile industry holds a very significant position and
this sector is regarded as one of the country’s largest and most vital sectors. This research study has aimed
to examine the correlation between textile firms’ performance and the stock market, focusing on the PSX
particularly. We have opted quantitative research approach after gathering the secondary data from the
PSX and all financial reports of textile companies listed on the exchange. To assess the performance and
influence of the textile industry on the stock market, various financial indicators such as trading volume,
stock price, financial ratios and market capitalization are employed. The research tends to investigate the
correlation between the textile industry indicators and stock market performance which is done through
statistical analysis and regression models. The core objective of this study aims to shed light on the extent
to which the textile industry affects investors sentiments, market trends, and market volatility.
Furthermore, considering factors such as government policies, exports, global market trends, and foreign
investment, the research explores the mechanism through which it affects the textile industry and PSX.
This study also examines the interplay between the textile industry and other sectors within the stock
exchange, finding potential spill-over effects and interdependencies.
Keywords: - Textile industry, Pakistan Stock Exchange, economic impact, stock market performance,
value chain, government policies, global market dynamics, investor attitude, market stability.
Received: March 2, 2024. Revised: September 16, 2024. Accepted: October 14, 2024. Published: November 25, 2024.
1. Introduction
The textile sector can also be referred to as the
heartbeat of the economy of Pakistan which
distinguishes it from other industries. This sector
holds much significance contributing massively
to the GDP of Pakistan as well as creating loads
of jobs, this sector with its deep roots and access
to abundant resources, has made a name for
itself in the global textile market. Talking about
the sector our approach is about a wide array of
activities, from creating fibers to whipping up
garments, all of them playing a major part in the
country’s manufacturing and export.[1]
The textile sector in the heart of Pakistan’s
economy stands tall, heavily contributing to both
jobs and exports. It is like the engine of a car
that drives economic growth when it comes to
ranking in foreign cash and boosting industrial
development especially. Taking 2020, for
example - Pakistan’s textile exports hit a
massive $12.4 billion, jumping up to 7% from
the year before.[2]
Then there is the Pakistan Stock Exchange
(PSX) in Pakistan is the main stage for trading
shares. The stock market is not generally more
than just numbers and graphs; rather it is a
mirror reflecting the economic health of the
country which is watched keenly by the
investors and decision-makers. And guess what
really shakes things up in the stock market? Yes,
economic shifts, the political weather, and how
different sectors, like our textile sector, are
doing.[3]
Speaking of which, the textile sector's
performance is important for the PSX. A lot of
the heavy hitters in textiles are listed there, and
their difficulties can send ripples across the
whole stock market, especially when times get
tough economically.[4]
This thesis? It is all about diving deep into how
the textile sector influences the PSX. It is not
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
101
Volume 4, 2024
just about looking at share prices of textile
companies on the PSX;[5] it is about
understanding how this sector weaves into
Pakistan's broader economic tapestry. But there
is more. Understanding the textile sector's sway
over the PSX is crucial for everyone in the mix –
investors, policymakers, the whole shebang. It is
not just number crunching; it is unearthing the
gears that drive the stock market, seeing how the
textile sector propels the economic machine.[6]
This study does not aim to be more than just
words on paper. It is about laying down hard
facts between the textile sector and the PSX. For
big players, this is the kind of knowledge that
will guide them in making smarter choices.
Moreover, this study is a stepping-stone for
future research into how different sectors can stir
the stock market and the broader economic
landscape in Pakistan.[7]
In the broader economy of Pakistan, the impact
of the textile sector raises intriguing questions
about its influence on the stock market. It is
crucial for policymakers, investors, and market
participants to fully understand the relationship
between the textile sector and the PSX, this will
help them to make informed decisions as well as
help them to capitalize on potential investment
opportunities. By examining the relationship,
what we can gain is insight into the dynamics of
the Stock market and the role of the textile
sector as a driving force of economic growth [8].
This study aims to make existing Policymakers,
investors, and other stakeholders of the textile
industry literate and informed by giving them
evidence of the relationship between the textile
sector and the Pakistan stock exchange. This
study and its findings will be a help for investors
and policymakers to make informed decisions be
it related to investment and business activities in
the textile industry. Furthermore, the core aim of
this study is to serve as a basis for future
research on the sectoral impact and performance
on the stock market and the economy of
Pakistan. [9].
Earlier studies have shown and discussed the
relationship between the impact of various
industries and stock markets in different
countries, whereas limited research has been
conducted on specifically discussing the impact
of the textile sector on the PSX in Pakistan.
Hence, the aim of this research is to find the gap
in the research and investigate how the
performance of the textile sector and
fluctuations in the stock market interplay with
each other. [10].
The gap in the research is between
understanding how the textile sector specifically
affects the PSX and to study and shed light on
the interplay between textile fluctuations and the
stock market in Pakistan.
1.1 The study attempts to achieve the
following goals
The objective of the research is to analyze and
examine the historical performance and long-
term trends of the textile sector in Pakistan, this
includes factors such as production ability,
market share, export volume, and employment
generation. [11].
Analyzing and understanding the trends and
fluctuations in the Pakistan Stock Exchange,
with market indicators including such as KSE-
100 Index, market capitalization, liquidity, and
trading volume. [12].
This research work intends to investigate the
relationship between the textile sector as well as
the performance of the stock market, examining
and analyzing whether there are any co-
movements and correlations. [13].
The research has assessed key macroeconomic
indicators, such as inflation rates, interest rates,
exchange rates, and government policies on the
textile sector and stock market.
The core objective and aim of this research is to
provide recommendations to investors, market
participants, and policymakers to help them
enhance their investment strategies, risk
management, and decision-making.[14].
The final result of this study will make the
people of Pakistan more knowledgeable by
making them understand the connection between
industry sectors and stock markets. Moreover,
this research and study will conclude and have
practical implications for policymakers and
investors, making a clear understanding of the
dynamics of the textile sector and its influence
on the stock market. [15].
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
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2. Methodology:
2.1 Research Design:
This research has built a link between the
Pakistan Stock Exchange (PSX) and the textile
sector's performance. The core focus is to find
an answer to the question of how the textile
sector influences the dependent variable and the
performance of the stock exchange. [16].
2.2 Data Type and Analysis:
The research has used statistical methods to
explore the connection between numerous
factors in the research. For this, the analysis
has revolved around the quantitative data.
After employing various statistical tools and
techniques it has used quantitative analysis
to investigate the patterns and relationships
between the textile sector and the stock
exchange.[17].
2.3 Data Collection:
For this research study, secondary data has
been gathered. This includes key
information about the textile sector and the
stock exchange performance, which is
sourced from financial reports, stock market
indices, economic indicators, industry
publications, and government reports. The
mentioned sources are crucial for the
analysis of how the textile sector affects the
stock exchange, offering a wide range and
reliability in the information provided.[18].
2.4 Variables and Instruments:
In this research, the main factors that are
analyzed are the performance of the textile
sector and the Pakistan Stock Exchange
(PSX). Generally, in order to evaluate the
performance of the textile sector you
analyze financial statements, production
data, and export data along with other
relevant indicators specific to the sector.
Meanwhile, in order to gauge the
performance of the stock market we look at
market indices, trading volumes, and the
trends in price movements.[19].
2.5 Data Analysis:
Descriptive statistical analysis like mean,
median, mode, and standard deviation are
used to examine the gathered data in order to
break down and make suitable trends and
variations. The purpose of these techniques
is to give us a deeper understanding of how
the stock exchange and the textile sector are
performing. Moreover, through these
techniques correlation analysis to investigate
how the stock exchange performance and
textile sector are linked. This kind of
analysis are key in figuring out how strong
and in what way these variables are
connected. [20].
2.6 Limitations:
It is important to understand the limitations
of our research as well. The first concern is
the potential bias in secondary data since the
source of the data is from a secured and
reliable public source. Lastly, we are assured
that the data is correct and complete. What
is more, there are outside factors and events
that we cannot cover in this study that might
affect how the stock exchange does, and we
cannot control everything that could play a
role.[21].
2.7 Mean:
The mean, a measure of central tendency, is
the average value of a set of data. The
computation involves dividing the total
number of observations by the sum of all
values included in the dataset. The mean is
vulnerable to extreme values and can be
affected by outliers.
2.8 Median:
The median, another measure of central
tendency, is the middle value when a dataset
is sorted in either ascending or descending
order. To find the median, you sort the data
and select the value that falls in the middle.
When there are an even number of
observations, the median is calculated by
averaging the two middle values.
2.9 Mode:
The value that appears the most often in a
dataset is its mode. Put another way, it is the
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
103
Volume 4, 2024
observation that happens most often. A
dataset may have no mode (no value appears
more than once), one mode (unimodal), or
numerous modes (multimodal).
2.10 Standard Deviation:
The standard deviation quantifies the spread
or dispersion of a dataset. It measures how
far each observation deviates from the mean
on average. A higher standard deviation
shows more data variability, while a smaller
standard deviation indicates less data
variability.[22].
2.11 Minimum and Maximum:
The smallest and greatest values in a dataset
are represented, respectively, by the
minimum and maximum values. The highest
value that has been seen is the maximum,
while the lowest value is the minimum.
2.12 Range:
The difference between a dataset's
maximum and minimum values is known as
its range. It provides a simple measure of the
spread of data by capturing the total extent
between the smallest and largest values [23].
2.13 MAPE (Mean Absolute Percentage
Error):
The average percentage difference between
the predicted and actual numbers is
calculated to decide the forecasting
accuracy, or MAPE. It is commonly used in
evaluating the performance of forecasting
models. MAPE = (1/n) * Σ (| (Actual -
Forecast)/Actual|) * 100 is the formula for
calculating MAPE.
2.14 MAD (Mean Absolute Deviation):
The MAD measure of dispersion yields the
average absolute difference between each
data point and the mean of the collection. It
provides a sign of the average deviation of
data points from the mean. The formula for
MAD is MAD = (1/n) * Σ (|Data point -
Mean|)
2.15 MSD (Mean Squared Deviation):
The average squared difference between
each data point and the dataset mean is
decided by the MSD, a measure of
dispersion. It shows the degree to which the
data points deviate from the mean. The
formula for MSD is MSD = (1/n) * Σ ((Data
point - Mean) ^2)
3. Results and Discussion
Company A: The variable COMPANY A
has a mean value of 102.86 with a standard
error of 1.21. The data is moderately
dispersed, showed by the standard deviation
of 18.87 and the coefficient of variation of
18.35%. There is a range of 87.24, with the
least value being 55.25 and the largest value
being 142.49. The data's mode is 92.
Company B: For the variable COMPANY
B, the mean value is 28.996, and the
standard error is 0.349. The data has a small
standard deviation of 5.478 and a coefficient
of variation of 18.89%. The minimum and
maximum values are 17.150 and 39.100,
respectively, yielding a range of 21.950. The
data's mode is 32.
Company C: The variable COMPANY C
has a mean value of 105.19 with a standard
error of 0.544. A moderate spread in the data
is shown by a coefficient of variation of
7.66% and a standard deviation of 8.06. The
minimum and maximum values are 90.01
and 131.01, respectively, resulting in a range
of 41.00. The data's mode is 100.
Company D: COMPANY D has a mean
value of 47.615 and a standard error of
0.513. With a coefficient of variation of
16.91% and a standard deviation of 8.054,
the data show a moderate degree of
dispersion. The minimum and maximum
values are 32.740 and 61.600, respectively,
resulting in a range of 28.860. The data has
two modes at 37.5 and 50.
Company E: The variable COMPANY E
has a mean value of 69.963 and a standard
error of 0.322. The data has a 7.22%
coefficient of variation and a comparatively
low standard deviation of 5.053. The
minimum and maximum values are 55.100
and 77.500, respectively, resulting in a range
of 22. 400.The mode of the data is 74.
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
104
Volume 4, 2024
Company F: COMPANY F has a mean
value of 71.227 with a standard error of
0.617. At a coefficient of variation of
13.53% and a standard deviation of 9.640,
the data show a comparatively higher degree
of dispersion. The minimum and maximum
values are 52.500 and 92.810, respectively,
resulting in a range of 40.310. The mode of
the data is 78.
Company G: The variable COMPANY G
has a mean value of 57.466 and a standard
error of 0.456. With a coefficient of
variation of 10.71% and a standard deviation
of 6.153, the data shows a moderate degree
of dispersion. The minimum and maximum
values are 44.100 and 79.750, respectively,
resulting in a range of 35.650. 56 is the
data's mode.
Company H: COMPANY H has a mean
value of 100.25 with a standard error of
0.580. With a standard deviation of 9.10 and
a coefficient of variation of 9.08%, the data
show a moderate spread. The minimum and
maximum values are 83.90 and 124.89,
respectively, resulting in a range of 40.99.
The data's mode is 90.
Variable
N
Mean
Mode
StDev
Variance
CoefVar
Minimum
Maximum
Range
COMPANY A
228
69.813
59
69.5
110.807
15.08
54.3
92.57
38.57
COMPANY B
249
12.919
11.2
12.42
6.283
19.40
9.730
22.18
12.45
COMPANY C
198
72.639
67
69.99
98.291
13.65
60.0
105.55
45.55
COMPANY D
249
42.757
45,
44.9
51.913
16.85
29.85
56.0
26.15
COMPANY E
24
9
70.698
74
72.48
29.080
7.63
59.4
84.45
25.05
COMPANY F
22
6
61.330
62
61.75
5
51.33
12.11
47.66
78.26
30.60
COMPANY G
14
1
47.259
45
48.48
45.807
14.32
0.0
60.95
60.95
COMPANY H
24
9
81.014
81.5
81.50
52.572
8.95
67.34
98.96
31.62
Table 1 High Stock exchange market index time series data
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
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Based on these Table 1, the companies that are more stable across different variables are
COMPANY H and COMPANY E. It is important to note that stability can be evaluated from
various perspectives, and additional factors or criteria may be considered for a comprehensive
assessment.
Variable
N
Mean
Medi
an
Mo
de
StDev
Variance
CoefVar
Minimum
Maximum
Range
COMPANY A
228
68.05
1
67.3
70
57
10.478
109.789
15.40
52.300
87.310
35.010
COMPANY B
249
12.32
9
11.7
50
10.
4
2.294
5.263
18.61
9.160
20.060
20.060
COMPANY C
219
102.2
5
99.1
6
98
7.57
57.34
7.41
87.61
126.10
126.10
COMPANY D
249
41.43
5
43.5
00
45
6.983
48.765
16.85
28.800
54.020
54.020
COMPANY E
249
68.61
6
70.0
00
61
5.528
30.562
8.06
57.750
79.250
79.250
COMPANY F
226
59.19
4
59.8
35
51
7.223
52.165
12.20
46.200
76.800
76.800
COMPANY H
141
45.61
6
46.0
00
44
6.638
44.059
14.55
0.000
56.310
56.310
COMPANY H
249
78.51
5
79.0
00
73
6.955
48.375
8.86
65.000
94.510
94.510
Table 2 Low Stock exchange market index time series data
Based on these table 2, the company COMPANY A (Company A) appears to have higher
volatility and lower stability compared to other companies. Companies such as COMPANY C
(Fazal Cloth Mills), COMPANY E (Company E), and COMPANY H (Company H) show better
stability across the variables.
4. Trend Analysis
Variables
COMPANY
A
COMPANY
B
COMPANY
C
Model Types
MAPE
MAD
MSD
MAPE
MAD
MSD
MAPE
MAD
MSD
Linear
14.475
11.788
253.741
21.8726
4.4175
31.9977
5.4034
5.6822
55.6396
Quadratic
14.198
11.554
211.284
25.4186
4.4534
26.0404
5.3815
5.6577
55.1729
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
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Exponential
Growth
14.716
12.213
271.463
20.5468
4.5375
40.0427
5.3139
5.6049
55.7159
Table 3 Trend Analysis of textile companies
Variables
COMPANY D
COMPANY E
COMPANY F
Model Types
MAPE
MAD
MSD
MAPE
MAD
MSD
MAPE
MAD
MSD
Linear
15.5376
6.3266
54.5035
6.9844
4.6511
28.8983
11.9759
7.5806
83.99
Quadratic
6.5036
2.7030
11.0438
4.5830
3.0662
13.8338
7.7686
4.9714
36.64
Exponential Growth
15.6725
6.4881
55.5077
7.0050
4.6802
28.9484
11.8788
7.5969
85.62
Table 4 Trend Analysis of textile companies
Model Types
MAPE
MAD
MSD
Linear
7.7875
4.3189
40.0966
Quadratic
7.7412
4.2620
39.1240
Exponential Growth
11.8788
7.5969
Some data are non-positive; cannot fit the
growth model
Table 9 Trends Analysis for COMPANY H
Figure 1 Residual and Quadratic Plot for COMPANY A
Figure 2 Residual and Quadratic Plot for COMPANY B
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
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Volume 4, 2024
Figure 3 Residual and Quadratic Plot for COMPANY C
Figure 4 Residual and Quadratic Plot for COMPANY D
Figure 5 Residual and Quadratic Plot for COMPANY E
Figure 6 Residual and Quadratic Plot for COMPANY F
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
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Figure 7 Residual and Quadratic Plot for COMPANY H
5. Conclusion
When considering the trend analysis for the
companies, different models were evaluated for
their suitability. The quadratic model appeared
to be the most proper for Company A, Company
Ds, Company E, Company F, and Company H,
as it yielded the lowest values for metrics such
as MAPE, MAD, and MSD. However, for
Company C, Company G, and Company H, the
models showed similar performance across the
linear, quadratic, and exponential growth
options.
Based on the stability analysis, Company C,
Company G, and Company H were identified as
showing better stability compared to others.
However, when considering both stability and
trend analysis, it can be concluded that
Company C, with its high median value, and
Company H, with stable mode values, proved
consistent performance and were well-suited for
the quadratic trend model. Additionally,
Company G showed better stability with the
highest minimum value and a suitable linear
trend model.
Acknowledgment:
We would like to express our gratitude to the
Pakistan Stock Exchange (PSX) for providing us
with the data used in this analysis. Their
contribution has been invaluable in helping us
evaluate and compare the performance of
different models. We appreciate their support
and cooperation throughout this process.
Declaration of Generative AI and AI-assisted
technologies in the writing process
During the preparation of this work, the authors
used WriterBuddy in order to improve the style
and structure of the content to give it a
professional touch. After using this tool/service,
the authors reviewed and edited the content as
needed and take full responsibility for the
content of the publication.
Hence we would like to conclude that in the case
where the AI technology “Writerbuddy” was
used, it was done only in order to improve the
readability and language of the manuscript.
References:
[1] D. Baiardi and C. Morana, “Financial
development and income distribution
inequality in the euro area,” Econ Model,
vol. 70, pp. 40–55, Apr. 2018, Doi:
10.1016/j.econmod.2017.10.008.
[2] S. Parveen, Z. W. Satti, Q. A. Subhan, N.
Riaz, S. F. Baber, and T. Bashir,
“Examining investors’ sentiments,
behavioral biases and investment
decisions during COVID-19 in the
emerging stock market: a case of
Pakistan stock market,” Journal of
Economic and Administrative Sciences,
Jul. 2021, Doi: 10.1108/JEAS-08-2020-
0153/FULL/HTML.
[3] Z. Shamsuddin, A. M. M. Ahmad Kamel,
W. M. N. Wan Daud, and W. Salhi
Yusoff, “ANALYSIS OF CAPITAL
STRUCTURE AND FINANCIAL
PERFORMANCE IN THE
JORDANIAN INSURANCE SECTOR,”
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
109
Volume 4, 2024
Humanities & Social Sciences Reviews,
vol. 8, no. 3, pp. 1310–1320, Jun. 2020,
doi: 10.18510/HSSR.2020.83133.
[4] F. Khan and M. Asif Ali, “The
Relationship between Macroeconomic
Variables and the Dividend Payout Ratio
of the Textile Sector Listed on Pakistan
Stock Market Startup research project
View project Positive and Negative
Politeness: A Cross-Cultural Study of
Responding to Apologies by British and
Pakistani Speakers View project,” 2018,
doi: 10.31529/sjms.2018.4.1.9.
[5] D.-H. Kim and S.-C. Lin, “Income
inequality, inflation and financial
development,” J Empir Finance, vol. 72,
pp. 468–487, Jun. 2023, doi:
10.1016/J.JEMPFIN.2023.04.008.
[6] Z. Wang, Y. P. Teng, S. Wu, Y. Liu, and
X. Liu, “Geopolitical risk, financial
system and natural resources extraction:
Evidence from China,” Resources Policy,
vol. 82, May 2023, doi:
10.1016/j.resourpol.2023.103609.
[7] S. Amjed, “The impact of financial
structure on profitability: Study of
Pakistan’s Textile Sector,” MIBES, 2007,
Accessed: Jun. 06, 2023. [Online].
Available:
http://www.rhsmith.umd.edu/finance/gph
illips/research.html
[8] J. ichi Aoki, G. Takaku, and F. Ito,
“Aribatidae, a new myrmecophilous
oribatid mite family from java,” Int J
Acarol, vol. 20, no. 1, pp. 3–10, 1994,
doi: 10.1080/01647959408683994.
[9] “Profitability and Working Capital
Management Nexus: Evidence from
Food & Personal Care Products Sector
Firms Listed on Pakistan Stock Exchange
| Journal of Business & Tourism.”
https://jbt.org.pk/index.php/jbt/article/vie
w/90 (accessed Jun. 06, 2023).
[10] D. Acemoglu, S. Johnson, and J. A.
Robinson, “The colonial origins of
comparative development: An empirical
investigation,” American Economic
Review, vol. 91, no. 5, pp. 1369–1401,
2001, doi: 10.1257/AER.91.5.1369.
[11] C. Meniago and S. A. Asongu,
“Revisiting the finance-inequality nexus
in a panel of African countries,” Res Int
Bus Finance, vol. 46, pp. 399–419, Dec.
2018, doi: 10.1016/j.ribaf.2018.04.012.
[12] A. Ullah, C. Pinglu, S. Ullah, M. Zaman,
and S. H. Hashmi, “The nexus between
capital structure, firm-specific factors,
macroeconomic factors and financial
performance in the textile sector of
Pakistan,” 2020, doi:
10.1016/j.heliyon.2020.e04741.
[13] M. Muhammad, W. Ullah Jan, and K.
Ullah, “Working Capital Management
and Profitability An Analysis of Firms of
Textile Industry of Pakistan.”
[14] A. G. Khan, “The relationship of capital
structure decisions with firm
performance: A study of the engineering
sector of Pakistan,” International Journal
of Accounting and Financial Reporting,
vol. 2, no. 1, doi:
10.5296/ijafr.v2i1.1825.
[15] P. Ghafoor Awan Dean and M.-P.
Shumaila Bashir, “Determinants of
Capital Structure of Textile Industry in
Pakistan,” vol. 6, no. 1, 2016, Accessed:
Jun. 06, 2023. [Online]. Available:
www.iiste.org
[16] A. A. Alvi and U. Shaid, “Textile
Industry Crisis in Pakistan,” 2016.
[17] M. Usman and A. Hassan, “Performance
of Textile Sector of Pakistan: Application
of Data Envelopment Analysis Approach
FAIQ MAHMOOD,” 2014, Accessed:
Jun. 06, 2023. [Online]. Available:
www.irmbrjournal.com
[18] “View of Investors Perception About
Cash Conversion Cycle When Valuing
Firm (A study of Textile Sector of
Pakistan Stock Exchange).”
https://www.kasbitoric.com/index.php/kb
j/article/view/172/157 (accessed Jun. 06,
2023).
[19] F. Ahmed and D. A. Siddiqui, “Impact of
Debt Financing on Performance:
Evidence from Textile Sector of
Pakistan,” SSRN Electronic Journal, May
2019, doi: 10.2139/SSRN.3384213.
[20] R. G. Rajan and L. Zingales, “The great
reversals: the politics of financial
development in the twentieth century,” J
financ econ, vol. 69, no. 1, pp. 5–50, Jul.
International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
110
Volume 4, 2024
2003, doi: 10.1016/S0304-
405X(03)00125-9.
[21] S. Khan, H. Ullah, A. Fida, and S. Khan,
“The Impact of Ownership Structure on
Dividend Policy Evidence from
Emerging Markets KSE-100 Index
Pakistan,” International Journal of
Business and Social Science, vol. 3, no.
9, 2012, Accessed: Jun. 06, 2023.
[Online]. Available: www.ijbssnet.com
[22] S. Z. A. Shah, M. Ahmad, and F.
Mahmood, “Heuristic biases in
investment decision-making and
perceived market efficiency: A survey at
the Pakistan stock exchange,” Qualitative
Research in Financial Markets, vol. 10,
no. 1, pp. 85–110, 2018, doi:
10.1108/QRFM-04-2017-
0033/FULL/XML.
[23] M. Ahmad and S. Z. A. Shah,
“Overconfidence heuristic-driven bias in
investment decision-making and
performance: mediating effects of risk
perception and moderating effects of
financial literacy,” Journal of Economic
and Administrative Sciences, vol. 38, no.
1, pp. 60–90, Feb. 2022, doi:
10.1108/JEAS-07-2020-
0116/FULL/HTML.
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International Journal of Computational and Applied Mathematics & Computer Science
DOI: 10.37394/232028.2024.4.11
Talha Shakeel, Hamza Qayyum,
Shayan Fazal, Saleha Javed Kayani,
Dr. Danish Hasan, Usama Raza
E-ISSN: 2769-2477
111
Volume 4, 2024