




































Asian Finance & Banking Review 

 Vol. 5, No. 1; 2021 

                                       ISSN 2576-1161   E-ISSN 2576-1188 

Published by CRIBFB, USA 

 

54 

THE NEXUS BETWEEN FIRM SPECIFIC FACTORS, 

MACROECONOMIC FACTORS AND FIRM PERFORMANCE 

OF TEXTILE SECTOR OF BANGLADESH 

 

 

Imran Mahmud 

Independent Researcher 

Faculty of Business Studies 

University of Dhaka, Dhaka, Bangladesh 

 E-mail: Imran.mahmud.du@gmail.com  

 

Hossain Ahmmed Fahad 

Independent Researcher 

Faculty of Business Studies 

University of Dhaka, Dhaka, Bangladesh 

E-mail: Fahadhossain.frg@gmail.com 

 

Atkia Nabiha Rahman 

Independent Researcher 

Faculty of Business Studies 

University of Dhaka, Dhaka, Bangladesh 

E-mail: A.nabiharahman@gmail.com 

 

 

ABSTRACT 

The purpose of this study is to identify the determinant factors of the profitability of textile sector 

of Bangladesh. To achieve this, a sample of 31 textile companies for the period of 2011 to 2019 is 

used. Two dependent variables- return on assets ROA (accounting measure) and Tobin’s Q 

(market measure) are tested using both fixed effect model and Panel corrected standard error 

(PCSE) model. As the PCSE regression robustly suited the dataset, this is used to explain the 

impact of both firm specific factors and macro-economic factors on the performance of textile 

firms listed in Dhaka Stock Exchange. The determinants of profitability differ depending on which 

measures of profitability we have taken as proxy. When ROA is taken, age, debt to equity, debt to 

asset, growth, asset turnover, cost effectiveness (EATC) and export growth are found significant. 

Among them, debt to equity, asset turnover, cost effectiveness and export growth conforms to the 

expected positive sign. In terms of Tobin’s Q, age, size, debt to equity, growth, cost effectiveness 

(EATC), board size and export growth are found having significant impact on firm performance 

where age, EATC, board size, and export growth conforms to the expected positive sign. 

 

Keywords: Tobin's Q, Cross Sectional Dependence, Panel Data Analysis, PCSE Model, Textile 

Sector. 

 

JEL Classification Codes: C83, C87, L25. 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

55 

INTRODUCTION 

The economic growth and profitability of an industry contributes to the ultimate social welfare 

(Hermelo & Vassolo, 2007). Firm performance plays an important role in national economic 

growth and creation of employment in the country. Bangladesh is a South Asian country with a 

population of approximately 165 million. Textile as a whole is a heterogeneous product market 

with yarn, fabric, colors, brands, fashion, and material quality etc. all contributing to create 

economic value. Readymade garments (RMG) sector of our country has become one of the biggest 

earners of foreign currency. Billions of dollars as export earnings are received and millions of jobs 

are created by this sector each year. This sector comprises for 83% of total export earnings with a 

value of $33674 million during financial year 2019-2020.  

Growth in the textile sector can be prolonged through the active role of both private and 

public stakeholders. The variables that influence the textile sector and its performance should be 

identified so that policymakers can formulate policies to influence those factors to have significant 

positive impact on textile firm’s performance.  

One of the intriguing discussions in business economics is regarding the determinant 

factors that impact firms’ performance, comparing firm-specific as well as industry specific factors 

(Hintošová et al., 2020). There have been undeniable dominant opinions that choose firm specific 

factors over industrial factors to explain firm’s performance (Blažková & Dvouletý, 2018). In the 

light of resource-based theory, this study will focus in firm specific factors and macro-economic 

factors that influence the performance of textile sector. There are large empirical studies in this 

field around the world, however, few comparable works are conducted in Bangladesh. Kalam and 

Utsho (2020) had conducted a similar study to identify the impact of firm specific factors on the 

profitability of NBFI in Bangladesh using a panel data model. Islam and Khan (2019) investigated 

the factors affecting the firm’s performance of pharmaceutical industry of Bangladesh using a 

random effect analysis. But with regard to textile industry, there exist only few studies conducted.  

Hence, the goal of this paper is to assess the influence of firm-specific and macro-economic 

factors on the performance of firms operating in the Textile industry and thus to enrich the standing 

literature in this field. A firm-level panel dataset allows us to test the effect of selected factors on 

firms´ performance using panel data regression approach of Fixed effect model and Panel 

Corrected Standard Error linear regression model. 

The rest of the paper is designed as follows- section 2 is the literature review and 

hypothesis, section 3 contains the methodology, section 4 contains estimation and results, section 

5 discussion of the result, section 6 represents comparison of result with previous studies and 

section 7 provides the conclusions. 

 

LITERATURE REVIEW 

Two competing approaches seeking to explain firm performance have been segmented into the 

industrial theory approach (Competitive forces approach) and resource-based approach. 

Significant works on the debate of industry effects versus firm-specific effects as key performance 

indicator have led other researchers to provide a more in-depth view on the issue (Rumelt, 1991; 

Porter, 1980; Schumacher & Boland, 2005; Blažková & Dvouletý, 2018; Hanggraeni et al., 2019). 

McGahan and Porter (2002), using a broad dataset covering every sector within the USA, showed 

that firm-specific factors influence business long run and short run profitability significantly in 

contrast to industrial factors. The study also showed that the effects of firm-specific individual 

factors have differed across different sectors. Another study was done by Pervan et al. (2018) 

illustrated that both firm-specific factors and industrial and macro-economic factors significantly 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

56 

affect business performance but the impact of the former was shown to be greater. The resource-

based view theory can be used to explain the importance of individual internal firm-specific factors 

in a firm’s profitability (Barney, 1991). The theory is based on the premise that scarce resources 

that competitors cannot easily copy or imitate or substitute lead to superior performance.  

 Many studies have been conducted to identify firm-specific and macro-economic 

determinants in different countries in different sectors. Variables such as size, tangibility, growth, 

leverage level, liquidity ratio, and efficiency ratios are examined in studies to deal with internal 

determinants of firm performance with often inconclusive results. A pioneering study done by 

Chowdhury and Amin (2007) illustrated that working capital has a significant impact on the 

financial performance of pharmaceutical firms in Bangladesh. Kuntluru et al. (2008) conducted a 

study on the determinants of financial performance of Indian companies and found a negative 

relationship between debt ratio and profitability along with a positive relationship among firm size 

and growth and profitability. Stierwald (2010) took 961 large Australian firms and exhibited that 

lagged profit and firm size have the most contribution to performance and profitability. 

Pathirawasam (2011) found out that internal factors i.e., firm size, inventory ratio, debt ratio, and 

the quick ratio have a significant impact on the financial performance when measured by return 

on assets (ROA). The study of Pratheepan (2014) found out that size has a positive impact whereas 

leverage and liquidity have an insignificant impact on the profitability of manufacturing firms in 

Sri Lanka.    

 Bhutta and Hasan (2013) studied the firm-specific and macroeconomic factors on firm 

performance of food sector in Pakistan using multivariate regression analysis from 2002 through 

2006. Findings revealed a significant negative impact of size and profitability on firm performance. 

Moreover, tangibility, growth, and food inflation are observed to have an insignificant positive 

impact and debt to equity an insignificant negative impact.  

 Chhapra et al. (2012) studied the determinants of capital structure that affect the growth 

of firms of Textile Sector in Pakistan using data from 90 companies from 2005 to 2010. They used 

a linear regression model to examine how size, taxes, profitability, fixed assets have an impact on 

financial leverage. They found that fixed assets, sizes, taxes, and net profit do not have any 

significant impact on leverage, however, the size of the firm has a negative significant impact on 

leverage.  

 Abbas et al. (2013) used leverage, growth, size, etc. as firm-specific factors to determine 

their impact on the profitability of the Textile sector of Pakistan. They used panel data of different 

firms for the period 2005 to 2010 using a linear regression model. Findings of the study exhibited 

that leverage has negative while the size of the firm has a positive impact on the firm’s profitability. 

However, they failed to find any significant relationship of growth and liquidity on a firm’s 

performance.  

 Antoun et al. (2018) studied the relationship of financial performance with internal and 

external factors on a sample selected from banks in Central and Eastern Europe. They found that 

size, bank concentration, economic growth, business mix, inflation rate can significantly explain 

financial performance when measured with a performance index. Jelena et al. (2018) in their study 

on the determinants of profitability of medium and large agricultural companies in Serbia 

concluded that market share, sales revenue growth, insurance, export, and current ratio have a 

positive impact on firm performance. 

 Blažková and Dvouletý (2018) conducted a study to investigate the impact of firm-specific 

factors on the financial performance of the Czech food processing firm over 2003-2014 with 1804 

firms. The proxy for financial performance was taken as price-cost margin, return on assets and 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

57 

return on equity. They found a positive relationship between labor productivity and profitability, 

a negative relationship between leverage and profitability, and a positive relationship between age 

and firm size.  

 An exploratory study on the impact of firm-specific factors on profitability of life 

insurance companies in eight Asian countries covering a period of 2008-2014 using a panel data 

Random Model revealed that size, the volume of capital, and underwriting risk has a significant 

relationship with profitability. Besides, they found that premium growth, asset tangibility, and 

liquidity are insignificant indicators of profitability (Zainudin et al., 2018). 

 Vieira et al. (2019) performed a similar type of study on determinants of the Portuguese 

firms’ performance. The study covered 37 non-financial firms from 2010 to 2015. They tested 

three dependent variables using the Generalized Method of Moments. They found that in terms of 

the market variable of performance, firm-specific variables are not so important to explain. They 

also explained that investor sentiment and insider ownership more effectively explain firm 

performance. They ultimately infer that determinant of firm performance change in compliance 

with the way stakeholders define firm performance. Dakić et al. (2019) implemented a study on 

the determinants of business success of Serbian Food Processing companies. The findings showed 

that debt ratio, quick ratio, sales growth, firm size, and capital turnover ratio can affect firm 

performance when measured by return on assets (ROA). 

 Hintošová et al. (2020) used age, liquidity ratio, size, asset turnover, and cost-effectiveness 

as determinants of firm profitability on data from 2009 to 2017 for 48 firms in the metallurgical 

industry in Slovakia and found a significant ambiguous impact of liquidity and age, negative 

impact of asset turnover and positive effect of cost-effectiveness on firm’s profitability.  

 Ullah et al. (2020) conducted a similar study to determine the nexus between firm-specific, 

macroeconomic factors on the performance of the textile sector in Pakistan. The study was done 

on 90 textile firms for the period 2008 to 2017 forming unbalanced panel data. They found a 

negative significant impact of debt to equity, negative insignificant impact of asset turnover ratio, 

the positive impact of export growth and sales growth, and negative impact of firm size, tax 

payable, and debt to asset with insignificant impact on firm performance.  

 Kalam and Utsho (2020) in their study on firm-specific profitability indicators of the 

NBFIs in Bangladesh conducted a panel data analysis on 19 NBFIs from 2007 to 2017. The study 

used total interest income to total asset, non-operating income to total asset, operating expense to 

total asset, deposit to total asset, size, and Equity multiplier as explanatory variables and found 

mixed results about the significance and direction of the relationship.  

 Liu et al. (2020) undertook a study to identify the determinants of financial performance 

of agricultural companies in China with a data set of 39 listed companies for the period 2013-2018. 

The study tested internal firm-specific factors and external macro-economic factors where they 

have measured financial performance based on return on assets (ROA), return on sales (ROS), and 

return on equity (ROE). The results show that firm size, long term liability ratio, sales growth rate 

is positively related and debt ratio, capital intensity, and export intensity are negatively related to 

firm performance. Moreover, they found no significant impact of external factors on financial 

performance.  

 Islam and Khan (2019) published their work on determining the factors of profitability of 

pharmaceutical industry of Bangladesh using a Random Effect analysis. They used a sample size 

of 20 listed companies for a period 2007 to 2016. They found that sales, operating income, 

operating cost, return on equity, and total debt have a significant impact on profitability of 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

58 

pharmaceutical industry. They also found that in terms of macro-specific variables, GDP rate of 

inflation have a significant impact on firm performance. 

 

RESEARCH QUESTION 

 Is there any relationship between firm-specific factors and firm performance of the textile 

sector of Bangladesh? 

 Is there any relationship between macro-economic factors and firm performance of the 

textile sector of Bangladesh? 

 

METHOD 

Sample Selection 

We determined the appropriate sample size for a small population using the Cochran’s formula 

(Glen, 2021).  

1st step: determine the sample size for a larger population. 

𝒏𝟎 =
𝒁𝟐𝒑𝒒

𝒆𝟐
 

Where, n0 = size of sample for a large population; z = the value at a certain confidence interval 

found in the Z table; p= estimated proportion of the population, that is, the sample; q= 1-p. If z= 

1.64 at 10% significance level, p=0.5 so q=0.5, the sample size would be, n0=
𝟏.𝟔𝟒𝟐∗𝟎.𝟓∗𝟎.𝟓

𝟎.𝟏𝟐
  =67.24 

Step 2: determine the required sample size for a small population 

𝒏 =
𝒏𝟎

𝟏 +
(𝒏𝟎−𝟏)

𝑵

 

Where, n0 is Cochran’s appropriate sample size, N is the population and n are the adjusted sample 

size for the population in this study. 

As, n0 =67, N=54, so the appropriate sample size is, n =
𝟔𝟕

𝟏+
(𝟔𝟕−𝟏)

𝟓𝟒

=30.15 

 

Variables Selection 

Dependent Variables 

As there is no clear literature or view on which variables should be taken as the proxy of firm 

performance, we have considered two dependent variables. 

 One Accounting Measure:  In this study, we have defined financial performance, the 

dependent variable, by using return on total assets (ROTA). ROTA is also known as Basic Earning 

Power Ratio (BEP) ratio.  This variable has been adopted in former research as well (Ahmad et 

al., 2015; Hintošová et al., 2020; Hult et al., 2008; Mijić et al., 2014; Bayaraa, 2017; Kumari 

&Kumar, 2018; Fiala et al., 2020). 

 One Market Performance Measure: Tobin’s Q has been taken as a proxy for firm 

performance in its competitive market. Tobin’s Q is the modified version of q suggested by Chung 

and Pruitt (1994). According to the methodology of Singh et al. (2017), Tobin’s Q is calculated by 

dividing the summation of the firm’s market value of equity and total debt by the book value of 

total assets.   

 

Independent Variables 

The determinants of financial performance have been tested using firm-specific and macro-

economic specific predictors. Firm size, age, debt to equity (DE), debt to asset (DA), current ratio, 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

59 

board size, growth, asset turnover ratio, earning after tax as % of total cost (EATC) are the 

representative of firm specific factors whereas taxation, GDP and export growth is selected as 

macro-economic factors.  

 The first variable is size of the firm. Most studies have proved that larger firms have 

higher profit generating ability thus showed a positive impact on financial performance 

(Asimakopoulus et al., 2009; Nunes et al., 2009; Pratheepan, 2014; Nakatani, 2019, Abbas et al., 

2013; Majumdar & Chhibber, 1999; Hermelo & Vassolo, 2007). Goddard et al. (2005) however 

found a negative size-profitability relationship. 

 Age of the firm as a determinant factor is supposed to have bi-directional impact on firm 

performance (Coad et al., 2013; Blažková & Dvouletý, 2019). They found that aging firm boosts 

productivity level and higher profits but slow down business performance. Cowling et al. (2018) 

on the other hand gathered proof of negative firm age-profitability relationship.  

 Current ratio is taken as a proxy for liquidity from the work of (Nanda & Panda, 2018; 

Jelena et al., 2018). Liquidity is found to be positively related to profitability (Goddard et al., 2005; 

Nanda & Panda, 2018; Yameen et al., 2019) as well as none or negatively related to profitability 

(Zainudin et al., 2018).  

 Asset turnover ratio, calculated by total sales to total assets, if higher indicates firm is 

efficiently using its assets to generate revenue. Ahmad et al. (2015) found a positive relationship 

between asset turnover ratio and profitability. Growth is measured as annual growth in assets 

following the work of Abbas et al. (2013) and Glancey (1998).  Where Jelena et al. (2018) and 

Singh et al. (2019) found positive impact of growth, however, Ahmad et al. (2015) found a negative 

impact of growth on firm performance.  

 Within the resource-based view theory, it is said that manufacturing capabilities are 

generated internally which is inimitable non-transferable (Chavez et al., 2017). To incorporate this 

view, cost effectiveness has been taken as a determinant variable. Earning after tax as a % of total 

cost (EATC) has been taken as a proxy for cost effectiveness. Hintošová et al. (2020) found a 

positive relationship between EATC and profitability. 

 Board of directors are responsible to protect shareholders’ interest by restricting conflicts 

of interest between managers and shareholders. Guest (2009) and Marcelo et al. (2014) conducting 

a study on UK firms and Portuguese firms respectively found out that board size has a significant 

negative impact on profitability suggesting a weak monitoring and malfunction of boards’ advisory 

role. On the other hand, Daily et al. (2003) and Drobetz et al. (2004) concluded a positive board 

size-profitability relationship.  

 Another important factor is leverage. To calculate leverage, debt to assets and debt to 

equity will be used following the methodology of Ullah et al. (2020). Most empiral studies have 

found an inverse leverage-profitability relationship (Asimakopoulus et al., 2009; Nunes et al., 

2009; Khaled & Samman, 2015; Nanda & Panda, 2018; Blažková & Dvouletý, 2019).   

 Taxation is calculated as income tax expense over earnings before taxes (EBT) following 

the methodology of Ullah et al. (2020). According to trade-off theory, the effective tax rate and 

profitability has a significant positive relationship as higher tax rate reduces the debt costs. 

Chhapra et al. (2012) found an insignificant relationship between tax and financial leverage 

whereas Ullah et al. (2020) found a significant negative relationship between profitability and tax 

rate.   

 Export is found to be positively related to firm performance of agricultural companies 

from the study of Jelena et al. (2018). Ullah et al. (2020) also found a significant positive export-



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

60 

profitability relationship on the textile firms of Pakistan. Natural logarithm of export growth has 

been taken as proxy of for this variable.  

 The last macro-economic variable selected for the study is real GDP. Bangladesh is 

speeding up with competition in regards of GDP. Gan et al. (2006) and Kosmidou (2008) found a 

positive relationship between GDP and performance. 

 

Table 1. Definition of Variables Used in Study 

 

Variables Notations Description Expected Sign 

Dependent Variables 

Profitability 
ROA Ratio of Net EBIT to Total Assets  

Tobin’s Q Ratio of (MVE +Debt) to Total Assets  

Independent Variables 

Control Age 
Difference Between Year of Establishment and 

the Year in Consideration 
+ 

Firm Specific 

Ln_Size Logarithm of Total Assets + 

DE Ratio of Interest bearing Debt to Total Equity -/+ 

DA Ratio of Interest Bearing Debt to Total Assets -/+ 

TATO Ratio of Total Sales to Total Assets + 

Growth Annual Growth in Assets + 

Liquidity Ratio of Current Asset to Current Liabilities + 

EATC Earnings After Taxes as a Ratio of Total Costs + 

Board Size Natural Logarithm of Board Size + 

Macro-Economic 

Export Growth 
Variation in the Natural Logarithm of Total 

Exports (Textile Sector). 
+ 

Taxation 
Ratio of Income Tax Expenses Over Income 

Earned Before Taxes (EBT) 
+ 

Real GDP Nominal GDP Less Inflation Rate + 

Note: ROA: Return on Total Asset; EBIT: Earnings Before Interest Tax; MVE: Market Value Equity; Ln: Natural 

Logarithm; DE: Debt-Equity; DA: Debt-Asset; TATO: Total Asset Turnover; EBT: Earnings Before Tax; GDP: Gross 

Domestic Product. 

 

Hypotheses  

Based on the literature review, the major policy questions of this paper is to identify how various 

predictor determinants affects the firm’s performance of textile sector in Bangladesh and whether 

these factors have positive or negative impact on performance. Incidentally, the hypotheses to be 

tested are given as follows: 

Firm Specific factors 

 H0: Firm size has no impact on firm’s profitability 

 H0: Firm age has no impact on firm’s profitability 

 H0: Debt to equity has no impact on firm’s profitability 

 H0: Debt to asset has no impact on firm’s profitability 

 H0: Asset turnover ratio has no impact on firm’s profitability 

 H0: Growth has no impact on firm’s profitability 

 H0: liquidity has no impact on firm’s profitability 

 H0: Earning after tax as a % of total cost has no impact on firm’s profitability 

 H0: Board size has no impact on firm’s profitability 

Macro-economic factors 

 H0: Export has no impact on firm’s profitability 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

61 

 H0: Taxation has no impact on firm’s profitability 

 H0: GDP has no impact on firm’s profitability 

 

Model of the Study 

Using the determinants selected, the models that are germane to this study to analyze the 

hypothesized relationship between dependent and independent variable are- 

 

ROA=α + β1Ageit + β2lnSizeit + β3DEit + β4DAit + β5CRit + β6Growthit + β7TATOit + 

β8EATCit + β9Taxationit + β10Ex_growthit + β11BoardSizeit + β12GDPit + uit + εit  

Tobin’s Q=α + β1Ageit + β2lnSizeit + β3DEit + β4DAit + β5CRit + β6Growthit + β7TATOit + 

β8EATCit + β9Taxationit + β10Ex_growthit + β11BoardSizeit + β12GDPit + uit + εit  

 

Where i = 1, 2, …. n and t = 1, 2, … t representing firm and year respectively; β is the parameter 

and ε represents the error term or disturbance and u represents the unobserved variations. 

 

 

EMPIRICAL RESULT 

Descriptive Statistics 

First the study shows the mean, standard deviation, minimum and maximum value for total 31 

companies with 279 observation of the textile sector. Then we test the dataset against the 

assumptions of normal distribution to show what regression model fits our dataset best.  

 

Table 2. Descriptive Statistics of Study 

 

Variables Obs Mean Std. Deviation Min Max 

ROA 279 0.071 0.059 -0.215 0.308 

NPM 279 0.034 0.243 -3.147 0.745 

TobinsQ 279 1.824 2.645 -1.353 25.007 

Age 279 20.871 10.781 4.000 57.000 

ln_Size 279 21.492 1.071 18.622 23.602 

DE 279 0.692 0.768 -1.672 4.796 

DA 279 0.332 0.287 0.000 2.517 

CR 279 2.049 2.374 0.084 14.673 

Growth 279 0.213 0.459 -0.800 3.095 

TATO 279 0.761 0.639 0.065 4.104 

EATC 279 0.059 0.117 -0.759 0.779 

Taxation 279 0.152 0.272 -3.553 0.881 

Ex_Gr 279 0.113 0.116 0.002 0.4335 

BdSize 279 6.867 1.702 2 12 

Real_GDP 279 0.069 0.007 0.060 0.082 
Source: Author’s Calculation 

 

 

 

 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

62 

Multicollinearity Test 

Table 3. Result of Pearson Correlation Coefficient of Predictor Variables 

 

 Age Size DE DA CR Growth TATO EATC 
Ta

x 
Ex_Gr BdSize GDP 

Age 1.00            

Size -0.42 1.00           

DE 0.09 -0.02 1.00          

DA 0.05 -0.33 0.18 1.00         

CR -0.19 0.06 -0.21 -0.26 1.00        

Growth -0.09 0.20 -0.03 -0.14 0.02 1.00       

TATO 0.24 -0.43 0.06 0.06 -0.18 -0.14 1.00      

EATC -0.28 0.20 -0.19 -0.31 0.21 0.19 -0.14 1.00     

Tax 0.03 -0.00 -0.07 -0.14 -0.01 0.04 0.22 0.05 
1.0

0 
   

Ex_Gr -0.13 -0.12 0.11 0.06 -0.05 0.05 0.05 0.09 
0.0

2 
1.00   

BdSize 0.22 0.17 -0.10 0.06 -0.07 0.05 -0.02 -0.10 

-

0.0

2 

-0.10 1.00  

GDP 0.21 0.15 0.02 0.10 -0.01 -0.01 -0.08 -0.22 
0.0

5 
-0.26 0.06 1.00 

Source: Author’s Calculation 

 

The results shown in the table shows that there are no multicollinearity problems among the 

independent determinant variables selected in this study. 

 

Table 4. Results of VIF Test 

 
Variables VIF 1/VIF 

Ln_Size 2.22 0.45 

Age 1.77 0.56 

DA 1.52 0.66 

TATO 1.38 0.72 

Real_GDP 1.31 0.77 

EATC 1.30 0.77 

Ln_BdSize 1.26 0.79 

CR 1.22 0.82 

DE 1.18 0.85 

Ex_Gr 1.14 0.88 

Taxation 1.10 0.91 

Growth 1.08 0.92 

Mean 1.37  

Source: Author’s Calculation 

 

The VIF value for the selected variables that are tested in this paper is 1.37 on an average which 

indicates that the multicollinearity problems do not exist in this model. 

 

 

 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

63 

Heteroscedasticity Test 

Table 5. Results of Heteroscedasticity Test 

 
Breusch Pagan /Cook-Weisberg test for heteroscedasticity 

Model Chi2(1) Prob>Chi2 Presence of Heteroscedasticity 

Model 1-ROA 11.33 0.00*** YES 

Model 2-Tobin’s Q 86.29 0.00*** YES 

Note: * represents significance at 10% level, ** represents significance at 5% level and *** represents significance at 

1% level. 

Source: Author’s Calculation 

 

The table shows the summary of Breusch-Pagan test. 

 

Autocorrelation Test 

Table 6. Results of Autocorrelation Test 

 
Wooldridge test for autocorrelation in panel data 

Model Chi2(1) Prob>Chi2 Presence of Autocorrelation 

Model 1-ROA 3.428 0.0740* YES 

Model 2-Tobins Q 24.133 0.0000*** YES 

Note: * represents significance at 10% level, ** represents significance at 5% level and *** represents significance at 

1% level. 
Source: Author’s Calculation 

 

Table shows the result of autocorrelation test for the model of this study using Wooldridge 

et al. (1995) test. 

 

Test for Cross Sectional Dependency 

To see whether our model suffer from cross-sectional dependency, the xtcsd command in STATA 

has been used following the methodology of Pesaran (2004) and Friedman (1937). 

 

Table 7. Results of Test for Cross Sectional Dependency 

 
Model Pesaran’s 

value 

Friedman

’s Value 

Avg. value of Off-

diagonal elements (abs) 

Cross-Sectional Dependency 

Model 1-ROTA -1.144 4.757 0.37 YES1 

Model 3-Tobin’s Q 2.542*** 16.80 0.38 YES2 

Sour Note: 1 & 2 represents that Pesaran’s value and Friedman’s value are ambiguous in terms of direction in which 

case the average off-diagonal value should be used as parameter. As off-diagonal value is greater than 0.25, so Model 

ROTA and Tobin’s Q have considered to have cross sectional dependency. 

Source: Author’s Calculation 

 

Table shows the result of pesaran’s test for cross-sectional dependency. The results clearly 

show that at a 10% significance level, two of these models suffer from cross-sectional dependency. 

 

Hausman Test 

To choose whether we should go with random effect or fixed effect model, the Hausman (1978) 

test is needed to be run. The null hypothesis of the test states that random effect should be used. 

The null hypothesis can be rejected at the significance level of 5%. 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

64 

Table 8. Results of Hausman Test 

 
Model Chi2(9) P value Decision 

Model 1-ROA 51.49 0.0000*** Fixed Effect Model 

Model 2-Tobins Q 16.06 0.1884 Random Effect Model 

Note: * represents significance at 10% level, ** represents significance at 5% level and *** represents significance at 

1% level. 

Source: Author’s Calculation 

 

As it is found that there is heteroscedasticity problem associated with each of the model in 

this study and the two model is suffering from serial correlation problem among error terms, the 

results generated from FE or RE model cannot be taken as utopia and unbiased. So, the model 

must be corrected for these problems in data set. Hence, the model selected for our study is PCSE 

model that is said to improve the robustness. 

 

Panel Corrected Standard Error (PCSE) Model 

PCSE model shows improved result when the dataset is balanced which is in agreement with our 

study  

 

Table 9. Summary of Prais-Winston Linear Regression PCSE Analysis for Model 1 & 2 

 

Variables 
Model 1-ROTA Model 2-Tobin’s Q 

Coef. Z P>│ z│ Coef. Z P>│ z│ 

Firm-Specific factors 

Age -0.01 -6.39 0.00*** 0.02 1.73 0.08* 

Ln_Size -0.01 -0.88 0.38 -0.30 -3.19 0.00*** 

DE 0.03 5.3 0.00*** -0.17 -2.02 0.04** 

DA -0.06 -3.26 0.00*** 0.35 1.20 0.23 

CR -0.01 -1.46 0.14 0.00 0.09 0.93 

Growth -0.01 -3.16 0.00*** -0.15 -2.38 0.02** 

TATO 0.04 5.8 0.00*** 0.11 0.69 0.49 

EATC 0.17 5.65 0.00*** 1.31 2.33 0.02** 

Ln Bd Size 0.01 0.3 0.76 0.34 2.05 0.04** 

Macro-economic Factors 

Taxation 0.01 0.3 0.77 -0.01 -0.19 0.85 

Ex_Growth 0.27 2.03 0.04** 8.00 2.60 0.01*** 

GDP -0.11 -0.4 0.69 5.52 0.81 0.42 

Constant 0.15 2.05 0.04** 6.19 2.81 0.01*** 

R-Sq 0.73 0.43 

Chi2 230.99 63.91 

Prob>Chi2 0.0000 0.0000 

Note: * represents significance at 10% level, ** represents significance at 5% level and *** represents significance at 

1% level. (Underline) represents wrong sign. 

Source: Author’s Calculation 

 

Table shows the result of PCSE regression analysis for each of the two model. For model 

1-ROA, the firm-specific factors i.e., size, Debt to equity (DE), asset turnover (TATO), cost 

efficiency measured by EATC, and board size have a positive impact on ROA and age of firm and 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

65 

size of the firm, DA, CR and Growth have a negative impact on net profit margin. However, among 

the independent firm-specific variables age, DE, DA, Growth, TATO and EATC have proved to 

be statistically significant. In terms of macro-economic variables, taxation and export growth have 

a positive impact on profitability and GDP growth has a negative impact on profitability. Among 

these variables, Export growth is being found to be statistically significant as P value is less than 

5%. The overall R2 is around 73%. In addition, the model is found to be valid as the F-value is 

positively large and p value is less than 5%. In other words, the goodness of fit of the model is 

pretty good in predicting the ROA of the textile sectors. 

For Model 2-Tobin’s Q, the PCSE model has a R2 value of 43% implying that the goodness 

of the fit of this model is somewhat acceptable and the model is justifiable as well since it has a F-

value of 63.91 and overall p value of lower than 5%. According to the PCSE model, age, debt to 

asset (DA), CR, asset turnover (TATO), cost efficiency (EATC) and board size, GDP growth and 

export growth have a positive impact on market replacement value of profitability whereas size of 

the firm, Debt to Equity (DE), Growth and Taxation have a negative impact on profitability. 

Among the firm-specific variables, age, size of the firm, DE, growth, EATC, board size is found 

to be significant. Among the macro-specific variables, only export growth is found to be 

statistically significant. 

 

COMPARISON OF STUDY RESULTS WITH PREVIOUS WORKS 

According to WTO, Bangladesh holds the 3rd position among the top 10 exporters of clothing in 

2019. China holds the 1st position in that list. India and Pakistan also took their position in that list 

too.  A comparison of our result with those of previous literature is shown below. 

 

Table 10. Comparison of the Study with Previous Literatures 

 

Comparison of results (Accounting measures of Profitability) 

Variable 
Expected 

Sign 

Textile sector 

of Bangladesh 

Previous literature 

(Sign & significance) 

Countries and sectors on the study 

has been conducted 

Firm-Specific factors 

Age + (-)√ 
Positive; Significant 

Negative; Significant 

Slovak, Metallurgic (2020) 

Bangladesh, Textile (2020) 

Size + (-)× 
Negative; Significant 

Positive; Significant 

Pakistan, Textile (2020) 

China, Agricultural (2020) 

DE -/+ (+)√ 
Negative; Significant 

Positive; Significant 

Pakistan, Textile (2020) 

India, Textile (2019) 

DA -/+ (-)√ 
Negative; Insignificant 

Negative; Insignificant 

Pakistan, Textile (2020) 

China, Agricultural (2020) 

CR + (-)× 

Positive; Insignificant 

Positive; Significant 

Positive; Significant 

China, Agricultural (2020) 

Slovak, Metallurgic (2020) 

India, Textile (2019) 

Growth + (-)√ Positive; Significant Pakistan, Textile (2020) 

TATO + (+)√ 
Negative; insignificant 

Negative; Insignificant 

Pakistan, Textile (2020) 

India, Textile (2019) 

EATC + (+)√ Positive; Significant Slovak, Metallurgic (2020) 

Board Size + (+)× Negative; Insignificant Portugal, Manufacturing (2019) 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

66 

Macro-economic factors 

Taxation + (+)× Positive; Insignificant Pakistan, Textile (2020) 

Ex_Growth + (+)√ Positive; Significant Pakistan, Textile (2020) 

GDP + (-)× 
Positive; Insignificant 

Positive; Insignificant 

China, Agricultural (2020) 

Portugal, Manufacturing (2019) 

 

FINDINGS AND DISCUSSIONS 

Findings 

This section is divided into two parts. First the study presents the findings of individual model in 

terms of PCSE regression analysis as this model corrects all the diagnostic issues with the dataset. 

Later the overall findings of the paper are discussed in this section. From the analysis it is lucid 

that there are a number of firm-specific and macro-specific factors that influence firm 

performance. The findings of individual model are disclosed below. 

 

Table 11. Summary Result of Model 1- ROA 

 
Variable Expected Sign PCSE Model 

Actual Sign Significance 

Firm-Specific factors 

Age + - √ 

Ln_Size + - × 

DE -/+ + √ 

DA -/+ - √ 

CR + - × 

Growth + - √ 

TATO + + √ 

EATC + + √ 

Board Size + + × 

Macro-economic factors 

Taxation + + × 

Ex_Growth + + √ 

GDP + - × 

 

In the first model, ROA is taken as a proxy for accounting profitability in textile sector. 

Our analysis found that in textile sector, as firm is aging, their performance is retrograding 

significantly. In our study, it is found that as size of the firm is increasing, firm performance is 

degrading but the result is not significant. The relationship between capital structure and 

profitability can be both positive and negative supported by two classes of theories: trade-off 

theory and pecking order theory. In our study, it is found that debt to equity is positively and 

significantly related to profitability. It can be supported by the fact that mean debt to equity ratio 

of textile sector is around 69% and the firms are getting the tax benefit due to this higher ratio. 

Liquidity CR is found to be negatively related to profitability but the result is insignificant. As the 

mean CR ratio of textile firm is 2.05, it can be concluded that excessive capital is being unutilized 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

67 

bringing down the profitability. Another paradoxical relationship found is between growth of 

assets and profitability at a significant level.  

In terms of macro-economic factors, taxation and export growth are found to be positively 

related to ROA. It agrees with the expected sign of the variables but only Export growth is found 

to be significant in result. GDP growth rate is found to be negatively but insignificantly related to 

profitability.  

 

Table 12. Summary Result of Model 2- Tobin’s Q 

 
Variable Expected Sign PCSE Model 

Actual Sign Significance 

Firm-Specific factors 

Age + + √ 

Ln_Size + - √ 

DE -/+ - √ 

DA -/+ + × 

CR + + × 

Growth + - √ 

TATO + + × 

EATC + + √ 

Board Size + + √ 

Macro-economic factors 

Taxation + - × 

Ex_Growth + + √ 

GDP + + × 

 

Model 2-Tobin’s Q represents profitability in terms of market performance and its 

replacement cost. It can be seen from the table that; age of the firm has a positive and significant 

impact on firm market performance of textile sector. Size of the firm and profitability is found to 

have a significantly negative paradoxical relationship with firm market performance. It is the same 

as the result found when profitability was measured by ROA. DE is found to have a negative 

significant impact on firm’s market replacement value. Debt to asset ratio shows a paradoxical 

relationship with firm performance but it was insignificant. Liquidity CR is found to be 

insignificant and positively related to firm value. Growth of asset is found to be negatively and 

significantly related to firm performance. TATO, EATC and board size are found to be positively 

and significantly related to firm performance just like when regressed with ROA as well.  

In terms of macro-economic factors, export growth and GDP are found to have a positive 

impact on firm market performance while taxation found to be negatively related to profitability. 

Only export growth is found to be significant. It agrees with the expected sign as well as the sign 

found from Model 1-ROA.  

 

Discussion of Findings 

Hence, the overall findings of this study are that both firm-specific and macro-economic variables 

have a significant impact on profitability of the Textile sectors in terms of the different measures 

of profitability. However, their impact on profitability rest on which variable has been selected as 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

68 

a proxy to measure profitability. Specifically, asset turnover (TATO), cost efficiency (EATC) and 

Export growth (Ln.ex_growth) are found to be the most significant and influential factor to 

determine the firm’s profitability. TATO, EATC and export growth are inferred to be positively 

related no matter which measures of profitability is selected. Age of the firm is another significant 

variable impacting firm’s performance. Yet the direction of its relationship with profitability 

depends on from which point of view profitability is being measured. Size of the asset base of the 

firms is found to be negatively related to profitability inferring that increased asset size is not being 

effectively utilized to generate profits as well. Another significant variable, Growth, has proved to 

be negatively related to profitability suggesting that within textile sector, business performance is 

retrograding with age implying the lower growth rates for these firms.  It urges textile firms need 

to be more efficient in terms of technology and labor utilization. Otherwise, profitability will see 

a downward trend despite its asset growth. Board size has a positive impact on profitability when 

measured by accounting book value terms (ROA) or market value measures (Tobin’s Q). Though 

it does not show significant result in terms of ROA, still it can be implied that the larger size of 

the board is skeptical in performing its monitoring role actively enhancing its profitability. 

Taxation has a positive impact on profitability in terms of book value profitability. It implies that, 

though having an insignificant relationship, firms paying a moderate level of tax accomplish 

greater profitability conforming to the trade-off theory of the higher the tax rate, the lower the debt 

costs. Lastly, debt to equity (DE) is found to have a positive relationship with profitability in terms 

of ROA as the dependent variables. It implies that the trade-off theory holds true in this case. 

Lastly, other than export growth, no macro-economic factors seem to provide a significant and 

consistent relationship with profitability. 

 

CONCLUSION AND IMPLICATIONS 

This study examines the firm-specific factors and macro-economic determinants of firm 

profitability of textile sector of Bangladesh. The results using the PCSE model suggest that the 

determinants of financial performance change depending on the variable taken as a proxy of firm 

profitability. Since managers and investors constitute varying interest, the relative strengths of any 

explanatory variables on both of these proxies of firm performance must be known. 

For the first model, ROA was used to measure firm performance. As growth and age of the 

firm has a negative paradoxical relationship with profitability, it advises that policymakers and 

management body of the firms of textile sectors must manage their firms more efficiently by 

introducing technological equipment so that the increased assets size and their experience in terms 

of increased leads to productivity instead of declining it. Moreover, policymakers should continue 

to give rebate and incentives so that our RMG products are most welcome in other countries and 

the textile industry remains competitive. 

Our second model incorporates Tobin’s Q as firm’s profitability. Leverage and size of the 

firm have negative impact on profitability. It implies that investors prefer growth of firm be 

equipped with subsequent profitable investment instead of underutilization and lower level of 

financial leverage. So, managers must look into this issue to increase profitability. In terms of 

macro-economic factors, only export growth is found to have a significant positive impact on 

profitability urging the policymakers to support export growth.  

This research may contribute to a wide range of stakeholders such as managers, investors, 

regulators, banks etc. However, this study has some limitations. This study can include only 31 

firms in its sample as market of our country is still small. Moreover, there are some other variables 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

69 

that could have influence on firm performance but were not considered in this study such as R&D 

intensity, innovation etc. This also gives a scope for further research. 

 

REFERENCES 

Antoun, R., Coskun, A., & Georgievski, B., (2018). Determinants of financial performance of 

banks in Central and Eastern Europe. Business and Economic Horizons, 14(3), 513-529. 

 

Abbas, A., Z. Bashir, S., Manzoor & Akram M. N. (2013). Determinants of firm’s financial 

performance: an empirical study on textile sector of Pakistan. Business Economic 

Research, 3(2), 76-84. 

 

Ahmad, W., Ahmed, T., & Shabbir, G. (2015). Determinants of textile firms’ profitability in 

Pakistan. Forman Journal of Economic Studies, 11, 87-101. 

 

Asimakopoulus, I., Samitas, A., & Papadogonas, T. (2009). Firm-specific and economy wide 

determinants of firm profitability: Greek evidence using panel data. Managerial Finance, 

35(11), 930-939. 

 

Barney, J.B. (1991). Firm resources and sustained and sustained competitive advantage. Journal 

of Management, 17(1), 99-120. 

 

Blažková, I., & Dvouletý, O. (2018). The causes of firm performance variation in the Czech food 

processing industry in the context of the outlier effect. Management Research Review, 

41(8), 968-986.  

 

Blažková, I., & Dvouletý, O. (2019). Investigating the differences in entrepreneurial success 

through the firm-specific factors: Microeconomic evidence from the Czech food industry. 

Journal of Entrepreneurship in Emerging Economies, 11(2), 154-176.  

 

Bayaraa, B. (2017). Financial performance determinants of organizations: The case of Mongolian 

companies. Journal of Competitiveness, 9(3) 22-33. 

 

Bhutta, N. T., & Hasan, A. (2013). Impact of firm specific factors on profitability of firms in food 

sector. Open Journal of Accounting, 2, 19-25. 

 

Chhapra, Umer, I., & Muhammad, A. (2012). Determinants of capital structuring: an empirical 

study of growth and financing behavior of firms of textile sector in Pakistan. MPRA Paper 

51068, University Library of Munich, Germany. 

 

Coad A., Segarra A., & Teruel M. (2013). Like milk or wine: Does firm performance improve 

with age?. Structural Change and Economic Dynamics, 24(1), 173-189. 

 

Cowling, M., Liu, W., & Zhang, N. (2018). Did firm age, experience, and access to finance count? 

SME performance after the global financial crisis. Journal of Evolutionary Economics, 

28(1), 77-100. 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

70 

Chowdhury, A., & Amin, M.M. (2007). Working capital management practiced in pharmaceutical 

companies listed in Dhaka stock exchange. BRAC University Journal, 4(2), 75-86. 

 

Chhibber, P.K., & Majumder, K.K. (1999).  Foreign ownershipand profitability: Property rights, 

control, and the performance of firms in Indian industry. Journal of Law and Economics, 

42(1), 209-238. 

 

Chavez R., Yu W., Jacobs M. A., & Feng, M. (2017). Manufacturing capability and organizational 

performance: The role of entrepreneurial orientation. International Journal of Production 

Economics, 184, 33-46. https://doi.org/10.1016/j.ijpe.2016.10.028 

 

Chung, H. K., & Pruitt, W. S. (1994). A simple approximation of Tobin’s Q. Financial 

Management, 23(3), 70-74. 

 

Dakić, S., Mijić, K., & Jakšić, D. (2019). Multiple regression approach to modelling determinants 

of business success based on financial statement: Evidence from food processing 

companies in the Republic of Serbia. Custos e Agronegocio on Line, 15(4), 485-501. 

 

Daily, C.M., Dalton, D.R., & Cannella, A.A. (2003). Corporate governance: decades of dialogue 

and data. The Academy of Management Review, 28(3), 371-382. 

 

Drobetz, W., Schillhofer, A., & Zimmermann, H. (2004). Corporate governance and expected 

stock returns: evidence from Germany. European Financial Management, 10(2), 267-293. 

 

Fiala, R., Hedija, V., Dvorak, J., & Jansky, J. (2020). Are profitable firms also financially healthy? 

Empirical evidence for pig-breeding sector. Custos e Agronegocio on Line, 16(1), 173-201. 

 

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis 

of variance. Journal of the American Statistical Association, 32, 675–701. 

 

Gan, C., Lee, M., Yong, H.H.A., & Zhang, J. (2006). Macroeconomic variables and stock market 

interactions: New Zealand evidence. Investment Management and Financial Innovation, 

3(4), 80-101. 

 

Goddard, J., Tavakoli, M., & Wilson J.O.S. (2005). Determinants of profitability in European 

manufacturing and services: Evidence from a dynamic panel model. Applied Financial 

Economics, 15(18), 1269-1282. 

 

Glen, S. (2021). Ample Size in Statistics (How to Find it): Excel, Cochran’s Formula, and General 

Tips" From StatisticsHowTo.com: Elementary Statistics for the rest of us! Retrieved from 

https://www.statisticshowto.com/probability-and-statistics/find-sample-size/  

 

Glancey, K. (1998). Determinants of growth and profitability in small entrepreneurial firms. 

International Journal of Entrepreneurial Behavior and Research, 4(1), 18-27. 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

71 

Guest, P. M. (2009). The impact of board size on firm performance: evidence from the UK. The 

European Journal of Finance, 15(4), 385-404. 

 

Hintošová, A. B., Bobenič T., Hajduová, Z., & Szajt M. (2020). The influence of firm-specific 

factors on firms´ performance. Polish Journal of Management Studies, 21(2), 115-128.  

 

Hult, G.T.M., Ketchen, D.J., Griffith, D.A., Chabowski, B. R., Hamman, M.K., Dykes, B.J., 

Pollitte, W.A., & Cavusgil, S.T. (2008). An assessment of the measurement of performance 

in international business research. Journal of International Business Studies, 39(6), 1064-

1080. 

 

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46, 1251-1271. 

https://doi.org/10.2307/1913827  

 

Hanggraeni, D., Slusarczyk B., Sulung L.A.K., & Subroto A., (2019). The Impact of internal, 

external and enterprise risk management on the performance of micro, small and medium 

enterprises. Sustainability, 11(7), 2172. https://doi.org/10.3390/su11072172 

 

Hermelo, F. D., & Vassolo, R. (2007). The determinants of firm’s growth: an empirical 

examination. Abante, Escuela de Administracion Pontificia Universidad Católica de Chile, 

10(1), 3-20. 

 

Islam, M.S., & Khan, S. (2019). The determinants of profitability of the pharmaceutical industry 

of Bangladesh: a random effect analysis. International Journal of Financial Research, 

10(2), 68-74. 

 

Jelena, A., Kristina, M., Vera, M., & Branimir, K. (2018). The modelling factors of agricultural 

companies performances. Custos e Agronegocio on Line, 14(4), 223-240. 

 

Kalam, A., & Utsho, M. I. (2020). Effects of firm specific factors on profitability of non-bank 

financial institutions in Bangladesh: evidenced from Dhaka Stock Exchange (DSE). 

International Journal of Science and Business, 4(11), 1-13. 

 

Khaled, A. J., & Samman, H. A. (2015). Determinants of profitability: evidence from industrial 

companies listed on Muscat securities market. Review of European Studies, 7(11), 303-

311. 

 

Kosmidou, K. (2008). The determinants of banks’ profits in Greece during the period of EU 

financial integration. Management Finance, 34(3), 146-159. 

 

Kennedy, P. (2003).  A Guide to Econometrics. MIT Press, Cambridge. 

 

Kuntluru, S., Muppani, V.R., & Khan, A.M. (2008). Financial performance of foreign and 

domestic owned companies in India. Journal of Asia-Pacific Business, 9(1), 28-54. 

 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

72 

Kumari, R., & Kumar, N. (2018). Determinants of firm performance: A conceptual analysis. 

Pacific Business Review International, 10(11), 133-140. 

 

Lujing Liu., Jian Xu., & Yue Shang. (2020). Determining factors of financial performance of 

agricultural listed companies of China. Custos e Agronegocio, 16(4), 297-314.  

 

Mcgahan, A.M., & Porter, M. E. (2002). What do we know about variance in accounting 

profitability? Management Science, 48(7), 834-851. 

 

Marcelo, J.L., Quirós, M., & Lisboa, I. (2014). The impact of family control on firm performance: 

evidence from Portugal and Spain. Journal of Family Business Strategy, 5(2), 156-168. 

 

Majumdar, S., & Chhibber, P. (1999). Capital structure and performance: evidence from a 

transition economy on an aspect of corporate governance. Public Choice, 98, 287-305. 

https://doi.org/10.1023/A:1018355127454. 

 

Mijić, K., Zekić, S., Jakšić, D., & Vuković, B. (2014).  Meat industry in Serbia: performance 

analysis of meat-processing and livestock companies. Custos e Agronegocio On Line, 

10(3), 124-144. 

 

Nunes M. P. J., Serrasqueiro Z. M., & Sequeira T. N., (2009). Profitability in Portuguese service 

industries: A panel data approach. Service Industries Journal, 29(5), 693-707. 

 

Nakatani R. (2019). Firm performance and corporate finance in New Zealand. Applied Economics 

Letters, 26(13), 1118-1124. 

 

Nanda, S., & Panda, A. K. (2018). The determinants of corporate profitability: an investigation of 

Indian manufacturing firms. International Journal of Emerging Markets, 13(1), 66-86. 

 

Pervan, M., Curak, M., & Kramaric, T. P. (2018). The influence of industry characteristics and 

dynamic capabilities on firms´ profitability. International Journal of Financial Studies, 

6(4), 1-19. 

 

Porter, M.E. (1980), Competitive Strategy. The Free Press, New York. 

 

Pathirawasam, C. (2011). Internal factors which determine financial performance of firms: with 

special reference to ownership concentration. Retrieved from 

https://www.slu.cz/opf/cz/informace/acta-academica-karviniensia/casopisy-aak/aak-

rocnik-2013/docs-2-2013/Pathirawasam.pdf/ 

 

Pratheepan, T. (2014). A panel data analysis of profitability determinants: Empirical results from 

Sri Lankan manufacturing companies. International Journal of Economics, Commerce and 

Management, 2(12), 1-9. 

 

Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge 

working papers in economics 0435, Faculty of Economics, University of Cambridge. 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

73 

Rumelt, R.P. (1991). How much does industry matter?. Strategic Management Journal, 12(3), 

167-185. 

 

Schumacher, S., & Boland, M. (2005). The effects of industry and firm resources on profitability 

in the food economy. Agribusiness, 21(1), 97-108. 

 

Stierwald, A. (2010). Determinants of profitability: An analysis of large Australian firms. 

Retrieved from 

https://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2010n03.

pdf  

 

Singh, K., Misra, M., Kumar, M., & Tiwari, V. (2019). A study on the determinants of financial 

performance of US agricultural cooperatives. Journal of Business Economics and 

Management, 20(4), 633-647. 

 

Singh, S.,Tabassum, N., Darwish, T. K., & Batsakis, G. (2017). Corporate governance and Tobin's 

Q as a measure of organizational performance. British journal of management, 29(4), 1-

20.  https://doi.org/10.1111/1467-8551.12237 

 

Ullah, A., Pinglu, C., Ullah, S., Zaman, M., & Hashmi, S. H. (2020). The nexus between capital 

structure, firm-specific factors, macroeconomic factors and financial performance in the 

textile sector of Pakistan. Heliyon, 6(4), 741-755. 

https://doi.org/10.1016/j.heliyon.2020.e04741. 

 

Vieira E. S., Neves M. E., & Dias A. G. (2019). Determinants of Portuguese firms´ financial 

performance: A panel data evidence. International Journal of Productivity and 

Performance Management, 68(7), 1323-1342. 

 

Wooldridge, J. M. (1995). Score diagnostics for linear models estimated by two stages least 

squares. In C. R. Rao, G. S. Maddala, P. C. B. Phillips, & T. N. Srinivasan (Eds.), Advances 

in Econometrics and Quantitative Economics (pp. 66-87). Oxford: Blackwell. 

 

Yameen, M., Farhan, N., & Tabash, M. (2019). The impact of liquidity on firms' performance: 

empirical investigation from indian pharmaceutical companies. Academic journal of 

interdisciplinary studies, 8(3), 212-220. https://doi.org/10.36941/ajis-2019-0019 

 

Zainudin R., Mahdzan, N. S. A., & Leong, E. S. (2018). Firm-specific internal determinants of 

profitability performance: An exploratory study of selected life insurance firms in Asia. 

International Journal of Asia Business Studies, 12(4), 533-550. 

 

APPENDICES 

 

List of textile companies 

 
Serial 

No 

Name Serial 

No 

Name 

1 Alhaj Textile Mills Limited 17 Paramount Textile Limited 



https://www.cribfb.com/journal/index.php/asfbr                          Asian Finance & Banking Review                               Vol. 5, No. 1; 2021 

74 

2 Alltex Industries Limited 18 Rahim Textile Mills Limited 

3 Anlimayarn Dyeing Limited 19 Regent Textile Mills Limited 

4 Apex Spinning and Knitting Mills Limited 20 R.N. Spinning Mills Limited 

5 Argon Denims Limited 21 Safko Spinning Mills limited 

6 Dragon Sweater and Spinning Limited 22 Saiham Cotton Mills Limited 

7 Dulamia Cotton Spinning Mills Limited 23 Saiham Textile Mills Limited 

8 Desh Garments Limited 24 Shasha Denims Limited 

9 Envoy Textiles Limited 25 Simtex Industries Limited 

10 H. R. Textile Mills Limited 26 Sonargaon Textiles Limited 

11 Maksons Spinning Mills Limited 27 Square Textiles Limited 

12 Malek Spinning Mills Limited 28 Stylecraft Limited 

13 Matin Spinning Mills Limited 29 Tosrifa Industries Limited 

14 Metro Spinning Limited 30 Zaheen Spinning Limited 

15 Mozaffar Hossain Spinning Mills Limited 31 Zahintex Industries Limited 

16 Prime Textile Spinning Mills Limited   

 

 

Copyrights 

Copyright for this article is retained by the author(s), with first publication rights granted to the 

journal. This is an open-access article distributed under the terms and conditions of the Creative 

Commons Attribution license (https://creativecommons.org/licenses/by/4.0/). 

 

 

 

 

 

 
 


