




































Indian Journal of Finance and Banking 

 Vol. 5, No. 2; 2021 

                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

 

28 

LEVERAGE INDUCED FINANCIAL DISTRESS OF 

MANUFACTURING FIRMS IN BANGLADESH:   

A COMPARISON BETWEEN LISTED MNCS AND DOMESTIC 

FIRMS BY APPLYING ALTMAN’S Z SCORE MODEL 
 

 

Dr. Syed Mohammad Khaled Rahman 

Associate Professor 

 Department of Business Administration 

Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh 

E-mail:kr15sust@gmail.com 

 

Dr. Md. Khairul Islam 

Professor 

Department of Business Administration 

Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh 

E-mail: khairuldba@gmail.com 

 

Md. Mofazzal Hossain  

Undergraduate Student of 4
th

 Year 2
nd

 Semester   

Department of Business Administration 

Shahjalal University of Science and Technology, Sylhet, Bangladesh 

E-mail: hossaintipusust@gmail.com 

 

ABSTRACT 

Financial distress arises from excessive debt capital. The aim of the study was to determine 

Altman’s Z score and show a comparative analysis of the effect of debt on Z scores of listed 

MNCs & domestic firms of Bangladesh over a 24-year period (1996-2019). The study was based 

on secondary data. Seven domestic firms and seven MNCs were selected as sample from six 

manufacturing industrial sectors. It was found that on an average one local firm was in grey 

zone and the rest 13 firms were in safe zone (Z scores> 2.99). Z scores for MNCs were 

substantially higher than for domestic firms. The Z score’s mean for MNCs was 5.398 compared 

to 4.155 for domestic firms. For domestic businesses, the Z score shifts by 0.001 or 0.24% for a 

1% change in the opposite direction of the overall debt. For a 1% rise in overall debt, the Z 

score of MNCs decreases by 0.005 or 0.073%.  Domestic companies should increase Z score by 

redesigning the capital structure and improving basic earning power. The study has practical 

implications for corporate managers, policymakers, investors, and government because future 

strategy, policy, and business performance depends on the zone in which the firms are situated. 

 

Keywords: Z Score, Financial, Distress, Debt, Zone. 

 

JEL Classification Codes: G30, G32, G39. 



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INTRODUCTION 

Financial distress is such a situation of a firm in which the financial obligations are met with 

difficulty. According to static capital structuretheory, a firm can borrow up to that level at which 

marginal benefit of tax shield equals the marginal cost of financial distress (Ross, Westerfield, & 

Jordan 2002). There is a limit to the use of debt and after the optimum debt level cost of financial 

distress out weight tax shield benefit of leverage. The extreme consequence of financial distress 

is bankruptcy. Altman (1983) developed a model to predict bankruptcy and financial distress of 

firms. Using Altman’s Z score model to know financial distress or bankruptcy probability has 

many implications to lenders, investors, regulatory authorities, government, auditors, and 

managers. 

Several previous research works on the capital structure of Bangladeshi manufacturing 

companies revealed that leverage ratios are relatively high and these ratios have an adverse 

impact on financial performance. Ali, Rahman, & Mahmud (2016) found that 50% of 

Bangladeshi textile companies were in the ‘distress’ zone. Jahur and Quadir (2012) have 

identified fund management drawbacks and poor profitability as some of the causes of financial 

distress of SMEs in Bangladesh. The magnitude of debt capital directly affects the fourth 

component and indirectly affects other components of the Z score model. So, further 

investigation is required to estimate the Z score to identify in which zones the firms are situated-

‘safe’, ‘grey’, or ‘distress’ as well as how Z score changes for a particular change in debt capital.  

Multi-National Companies (MNCs) operating in Bangladesh is performing satisfactorily. 

Compared to the year 2018, earnings of most listed MNCs have increased in 2019. Earnings per 

share of five MNCs have increased from the previous year. Only eleven listed MNCs account for 

24% of market capitalization in Dhaka Stock Exchange (Barman, 2020). MNCs’ profitability in 

2018 was also higher than the previous year (The New Age, 18/5/2019). From observing the 

financial performances of both types of companies, it can be inferred that MNCs are more 

financially sound than domestic companies. But the question is –‘how far local firms are lagging 

behind in financial soundness than MNCs?’ or ‘are financial distress level of local companies are 

significantly higher than MNCs?’ The present study attempts to seek an answer to this question.  

 

PROBLEM STATEMENT 

Financial distress has many adverse effects on a firm. Bankruptcy cost reduces a firm’s ability to 

use the borrowed fund for productive purposes. The probability that firms would be unable to 

provide their promised return to bondholders increases due to an increase in leverage ratios. 

Failure of repayment of borrowed capital to bondholders would result in the transfer of corporate 

assets’ ownership from shareholders to bondholders. Theoretically, in a bankruptcy situation, the 

value of an asset becomes equal to the value of the liability, and the value of equity becomes 0. 

Eventually, control of the firm goes to bondholders. Besides shareholders, creditors are also 

affected because their payoff reduces due to the incurrence of bankruptcy cost. They do not get 

the amount that the firm owes to them (Ross et al., 2002). Global financing option reduces due to 

COVID 19 which was revealed from the study of Afrina, Beg, Zayed, Hossain, and Shahi 

(2020). 

There is both direct and indirect cost of financial distress. Legal and administrative costs 

are the direct cost of financial distress and these are incurred for legal proceedings, auditing, and 

accounting activities, liquidation process etc. There are many indirect costs which are very 

difficult to measure. Distress impairs the ability of a firm to conduct normal business operation. 

Suppliers deny delivering materials on favorable credit terms. Customers switch to competitor 



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companies for purchasing their products. Sales decline and thus distressed firm losses its market 

share. Conflict between shareholders and bondholders rises for gaining corporate control. Top 

management body of the financially distressed firm spend their invaluable time to avoid 

bankruptcy rather than on core business activities which brings more trouble to the firm. 

Experienced and skilled employees switch to peer companies. Moreover, many productive 

programs and attractive projects are not undertaken due to shortage of cash. When the signal of 

distress is revealed then creditors raise their required rate of return which increases firm’s cost of 

capital. The incremental requirement is considered as compensation. Increase of bondholders’ 

fixed payoff reduces residual payoff of shareholders. Less cash flow to shareholders leads to 

decline in market value of shares (Brealey & Myers, 1996). Janardhanan and Uma (2020) in their 

study revealed that use of debt capital significantly influences shareholders’ return of financial 

institution. Akani and Ifechi (2017) in their research on 40 listed firms of Nigeria found that 

excessive debt reduced ROA and ROE significantly. From the present study it was found that 

local firms were more financially distressed and Z scores were more sensitive to change in debt 

than MNCs. The rest of the paper contain relevant literature in section 3, objectives, followed by 

hypothesis developmentin section 4. In section 5 methodology of the study were explained and 

in next section findings of the study were discussed through data analysis. In section 7 

concluding remarks were made along with policy implications. 

 

LITERATURE REVIEW 

Altman’s Z-Score model is used to measure thefinancial distress of a company. Ross, 

Westerfield, and Jaffe (2005) defined financial distress as a situation where the firm’s operating 

cash flows are not sufficient to meet the short-term commitments. Sajjan (2016) conducted 

research to predict the probability of bankruptcy of both manufacturing & service-oriented firms 

listed in Indian stock exchanges during 2011-2015. The study revealed that the majority of firms 

were financially distressed which may fall in bankruptcy situation in coming years. Pradhan 

(2014) in her study determined Z scores of three Indian commercial banks over the periods 2001-

2008 and by using these values and neural network, Z scores of future years were determined. 

Study found that in every year the scores were above 6 and every observation was in safe zone.  

Aasen (2011) in his thesis applied Altman’s Z-score model to estimate the financial distress of 

180 listed Norwegian firms. Study found that Z-score of manufacturing firms were less than non-

manufacturing firms. Financial distress was positively related with the issuance of equity. After 

summarizing the research works of Altman, Anjum (2012) concluded that distress could be 

successfully forecasted by using Z score model prior to 3 years. Khaddafi, Falahuddin, Heikal, & 

Nandari (2017) in their study have analyzed the Z score of 29 banks of Indonesia during 2011-

2013 and revealed that majority of the banks were financially distressed in every year and ‘safe 

zone’ banks were moving to ‘distress zone’.  

Johur and Quadir (2012) in their study investigated the cause and remedial measures of 

financial distress of SMEs in Bangladesh. They found that financial distress has adverse impact 

on sales, managerial capability, financial planning etc. The reasons for financial distress were 

poor fund management, poor financial control, poor profitability etc. Ali et al. (2016) estimated 

the financial soundness of 18 textile firms in Bangladesh using Altman’s Z score model. From 

the study it was revealed that 28% of the companies fall in safe zone, 22% fall in grey zone and 

50% fall in distress zone. Mizan and Hossain (2014) in their research measured the financial 

distress of cement industry of Bangladesh. Study found that among 5 companies, two were in 

safe zone and three were in distress zone. Mizan, Amin, & Rahman (2011) in their study aimed 



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to reveal the financial distress of listed pharmaceutical companies of Bangladesh. Study revealed 

that among 6 companies, two were in safe zone and have no bankruptcy probability. But other 4 

companies were most likely to fall in financial distress within few years. Gopalakrishnan, Gupta, 

Raja, Reddy, & Subbarao (2019) have undertaken a research work to assess the financial 

soundness of 10 Indian steel companies during 2014-2018. Study found that decrease of working 

capital was the main reason for financial instability. Out of 10 companies, two large and old steel 

companies were in distress zone and two small sized companies were in safe zone. Iheduru and 

Okoro (2018) investigated the determinants of retained earnings of listed manufacturing firms in 

Nigeria. Study found that oil price positively affects but exchange rate and interest rate adversely 

affect retention ratio of manufacturing firms. 

Hillary, Nyang’au, & Ngacho (2018) in their study analyzed the impact of financial 

solvency and strength on competence of listed manufacturing companies of Kenya during 2011-

2015. Financial health was measured by Z score model. Study revealed that debt-equity ratio as 

solvency proxy has negative and insignificant impact on financial performance. Among 9 firms, 

one firm was in grey zone, three firms were in distress zone and five firms were in safe zone. 

Egbunike, Ogbodo, & Ojimadu (2019) studied association between financial distress and 

profitability of 21 firms of consumer goods sector listed in Nigerian stock exchange. Study found 

that return on asset was positively related but gross profit margin was negatively related with 

financial distress. Masum and Johora (2012) in their study has estimated the Z score of 4 ceramic 

companies of Bangladesh to assess the financial soundness during 2006-07 to 2010-11. Study 

showed that in initial year of the study period, two firms were in distress zone and two firms 

were in grey zone. The firms of distress zone entered into safe zone in 2010-11 which implies 

that financial soundness of ceramic companies were increasing gradually. Ikpesu and Eboiyehi 

(2018) has examined the impact of capital structure on financial distress of Nigerian 

manufacturing firmsin their research. Long term debt to asset ratio was used as the indicator of 

capital structure and Altman’s Z score as for measuring the financial distress. Study found that 

long term debt to asset ratio has negative effect on financial distress while profitability, 

tangibility and firm age reduces financial distress. Sadiq, Kachollom, Dasuki, and Yusuf (2017) 

in their study revealed that appropriate composition of debt and equity capital maximize bank’s 

return on investment. 

OBJECTIVE OF THE STUDY 

The fundamental objective of the study was to examine the effect of debt financing on financial 

distress measured by Altman’s Z score. The distinctive aims are: 

a. To critically examine the financial distress of companies by computing Z score as well as 

its individual component and make a comparative analysis between MNCs and local 

firms. 

b. To explore the changes in Z score due to changes in debt of firms and make a 

comparative analysis between MNCs and local firms. 

c. To test the significance of difference in Z scores between local firms and MNCs.  

HYPOTHESIS OF THE STUDY 
Null hypothesis (H0): Z scores of domestic companies do not significantly differ from that of  

MNCs 

 

 



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METHODOLOGY OF THE STUDY 

Data and Sample 

Secondary and quantitative data were used in the study. Nature of data was Panel and collected 

from audited annual reports of manufacturing firms. Dhaka Stock Exchange (DSE) listed 

manufacturing companies constitute the population which was divided into two strata- local 

firms and MNCs. In manufacturing segment, listed MNCs operate in six industrial sectors, 

namely Pharmaceuticals & Chemicals, Engineering, Food & Allied, Tannery, Cement, and Fuel 

& Power. From these industrial sectors seven MNCs and seven local firms were chosen by 

applying stratified sampling technique. Two firms were taken from Pharmaceuticals & 

Chemicals industry and one firm from rest of five industrial sectors. Sample size was 14 (7+7). 

Study period was from year 1996 to 2019. 

Data Analysis Technique 

Some descriptive statistics such as mean, standard deviation, and standard error were used to 

analyze data. Besides these, inferential statistic such as independent sample t-test has been used 

to test the significance of difference in Z scores of two groups.  

Model Specification and Variable Description 
Financial distress of a company is measured through Altman’s Z-Score Model. Altman (1993) 

had developed Z-score Model to predict bankruptcy probability which is composed of the 

following function: 

 

Z = 1.2X1+ 1.4X2+3.3X3+0.6X4 + 1.0 X5 

Where X1=Working capital/Total Assets,  

X2= Retained Earnings/Total Assets 

X3= EBIT/Total Assets,  

X4= Market Value of Equity/ Book value of Total Liabilities  

X5= Sales /Total Assets 

 

A score of Z less than 2.675 indicates that a firm has a 95% chance of becoming bankrupt within 

one year. The area between 1.81 and 2.99 should be thought of as grey area. Bankruptcy would 

be predicted if Z≤1.81 and non-bankruptcy if Z ≥ 2.99.  

Mean values of all variables in every year were determined to quantify the results. By 

two phases, grand mean value were determined. First, for each year, the arithmetic mean values 

of the above 5 predictor factors of all 7 firms were evaluated and then the response variable [Z 

score] was determined for each year. Secondly, the average of yearly mean values of explanatory 

and explained variables of twenty four years (1996-2019) are estimated to obtain the broad mean 

value that is used to examine the effect of 4
th

 explanatory factor (Market Value of Equity / Book 

Value of Total Liabilities) on Z score. 

 

RESULTS AND DISCUSSION 

Z-Scores & Zones of Domestic Companies and MNCs 

Table 1 indicates that the mean Z scores of all local businesses was higher than 1.81, which 

impliesthat either firms were in the gray area or in the protected zone where bankruptcy was not 

probable. Across most of the years, SPL's Z score was far higher than other firms, suggesting 

that the firm has a higher capacity to meet financial obligations than other firms. In most of the 



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times, the POC's Z score was beyond 3, but it has been in the distress zone in recent times. 

Likewise, across the overall span, the Z score for AFL and AMCL was beyond 2 and it also 

exhibited a clear trend. Relatively significant variations are seen in companies' AAL and SPL 

and Z scores have decreased in recent years, but over the full duration, SPL was in a safe zone. 

In most years, since Z value was within 1.81 and 2.99, the AAL was in the gray zone. This state 

can be seen in AMCL from 1998 to 2007 as well as in the early periods of the AFL. The highest 

average Z score in SPL was reported to be 8.3 and the least value of 2.78 in AFL was reported. 

The Z-Score of MNCs in various periods is shown in Table 2. It is shown that Z scores 

of all firms are higher than 2.99 with the exception of some years, which implies that firms were 

in the safe area or much of the period there was no risk of insolvency. Across most of the BSC 

and HCL periods, the Z score was over 2.99. Over the entire period, GSK's and LBD's Z score 

were above 2.99. In many years, GSK's Z score was greater than other companies, which means 

that the financial solvency of the firm in those periods was higher than that of other firms. 

Throughout most of the periods, BSC and RBB's Z score was beyond 3 whereas that of GSK 

exceeds 5 across most of the periods. In SBD, relatively large variability is seen as a sudden rise 

and fall in the Z score from the table is reported. Owing to an unprecedented increase in the 

share price, the Z scores of several firms grew significantly in 2010. Stability is seen in many of 

the MNCs, except this year. The highest mean Z score was reported in GSK, which was 6.72, 

and the least score in BSC was reported at 4.08.  

 

Comparison of Average Z-Score between Domestic Companies and MNCs 
The comparison of the average Z-Score between the two forms of companies is shown in Table 1 

and 2. It is evident that Z scores for MNCs were higher relative to those of local firms over the 

entire study period. The Z score variation was not very high. The Z score for MNCs was above 

10 in 1996, but it subsequently declined and stayed steady at three to four until 2008. The score 

has been standing around 5 in recent periods. The mean Z score of local firms hovered around 3 

until 2008 and the score has reduced in past few years since 2016. The overall mean Z score is 

evaluated by finding the arithmetic mean of the average Z score across all 24 years. The average 

Z score for MNCs was 5,398, whereas that for local firms was 4,155.  

 

Analyzing the Effect of Leverage on Z score of Domestic Companies and MNCs 
Initially, the influence of the market value of equity to book value of the total liabilities ratio on 

the Z score is measured in order to objectively analyze the influence of leverage on the Z score 

of local companies and MNCs. The leverage effect on this ratio is then calculated. 

 

     Table 1. Z-Score and Zones of Domestic Companies 

Year AAL AFL AMCL BPL CCL POC SPL Avg. 
1996 1.923 2.316 6.305 3.565 4.645 4.687 6.688 4.304 

Grey Grey Safe Safe Safe Safe Safe Grey 
1997 2.238 2.354 8.054 3.911 7.754 4.203 7.975 5.213 

Grey Grey Safe Safe Safe Safe Safe Grey 
1998 2.415 2.347 2.312 2.446 8.424 4.262 7.488 4.242 

Grey Grey Grey Grey Safe Safe Safe Grey 
1999 2.527 2.286 2.620 2.381 5.469 3.874 3.653 3.259 

Grey Grey Grey Grey Safe Safe Safe Grey 
2000 2.463 2.277 2.759 2.564 6.578 3.811 3.916 3.481 



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Grey Grey Grey Grey Safe Safe Safe Grey 
2001 2.519 2.000 2.801 2.198 4.784 3.181 4.303 3.112 

Grey Grey Grey Grey Safe Safe Safe Grey 
2002 2.529 2.064 2.607 2.003 3.319 3.109 4.097 2.818 

Grey Grey Grey Grey Safe Safe Safe Grey 
2003 3.141 2.456 2.470 1.523 2.191 3.137 4.313 2.747 

Safe Grey Grey Distress Grey Safe Safe Safe 
2004 2.454 2.972 2.617 1.782 1.755 3.202 7.722 3.215 

Grey Grey Grey Distress Distress Safe Safe Grey 
2005 2.502 3.474 2.641 2.448 1.961 3.877 5.956 3.266 

Grey Safe Grey Grey Distress Safe Safe Grey 
2006 2.420 3.181 2.649 2.135 2.495 3.285 4.695 2.980 

Grey Safe Grey Grey Grey Safe Safe Grey 
2007 2.062 3.431 2.907 2.258 3.090 3.371 6.024 3.306 

Grey Safe Grey Grey Safe Safe Safe Grey 
2008 2.361 4.167 3.529 2.980 2.740 4.128 7.043 3.850 

Grey Safe Safe Grey Grey Safe Safe Grey 
2009 3.796 3.978 3.758 2.931 3.042 4.347 8.975 4.404 

Safe Safe Safe Grey Safe Safe Safe Safe 
2010 22.806 3.771 3.867 5.094 11.663 3.928 12.073 9.029 

Safe Safe Safe Safe Safe Safe Safe Safe 
2011 10.113 3.300 2.786 4.130 5.171 3.547 2.950 4.571 

Safe Safe Grey Safe Safe Safe Grey Safe 
2012 5.848 3.059 3.816 3.584 4.078 3.357 9.130 4.696 

Safe Safe Safe Safe Safe Safe Safe Safe 
2013 4.562 2.989 4.429 3.009 4.107 3.386 11.442 4.846 

Safe Safe Safe Safe Safe Safe Safe Safe 
2014 4.147 2.713 5.207 2.983 3.644 3.338 20.339 6.053 

Safe Grey Safe Grey Safe Safe Safe Safe 

2015 
2.600 2.520 3.690 3.376 3.198 3.068 28.341 6.685 
Grey Grey Safe Safe Safe Safe Safe Grey 

2016 
2.555 2.249 3.829 3.641 2.211 1.811 9.134 3.633 
Grey Grey Safe Safe Grey Grey Safe Grey 

2017 
2.360 2.349 4.184 3.951 1.969 1.119 8.488 3.489 
Grey Grey Safe Safe Grey Distress Safe Grey 

2018 
1.956 2.368 4.600 2.983 2.209 1.446 6.503 3.152 
Grey Grey Safe Grey Grey Distress Safe Grey 

2019 
1.517 2.227 4.952 2.586 2.691 1.505 8.187 3.381 

Distress Grey Safe Grey Grey Distress Safe Distress 
Average 3.909 2.785 3.725 2.936 4.133 3.291 8.310 4.155 

Safe Grey Safe Grey Safe Safe Safe Safe 
Source: Derived from Annual Reports (1996-2019) and DSE Reports 

 

      Table 2. Z-Scores and Zones of MNCs 

 

Year BSC BATB GSK HCL LBD RBB SBD Avg. 
1996 4.875 6.105 11.633 8.539 7.584 8.970 23.548 10.179 

Safe Safe Safe Safe Safe Safe Safe Safe 
1997 3.646 3.934 9.308 8.757 6.086 7.211 21.361 8.615 

Safe Safe Safe Safe Safe Safe Safe Safe 



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1998 3.155 3.006 5.604 3.608 3.251 3.991 9.347 4.566 
Safe Safe Safe Safe Safe Safe Safe Safe 

1999 3.079 2.477 5.564 4.143 3.087 3.566 5.684 3.943 
Safe Grey Safe Safe Safe Safe Safe Safe 

2000 3.095 3.016 7.224 6.788 3.086 4.547 6.121 4.840 
Safe Safe Safe Safe Safe Safe Safe Safe 

2001 3.108 3.866 5.837 5.321 3.061 3.086 5.105 4.198 
Safe Safe Safe Safe Safe Safe Safe Safe 

2002 3.180 2.772 5.501 3.648 3.599 2.078 5.272 3.721 
Safe Grey Safe Safe Safe Grey Safe Safe 

2003 3.359 2.510 3.887 1.298 3.991 3.485 5.200 3.390 
Safe Grey Safe Distress Safe Safe Safe Safe 

2004 3.080 2.931 6.518 1.673 3.882 4.386 4.248 3.817 
Safe Grey Safe Distress Safe Safe Safe Safe 

2005 3.255 2.066 7.140 1.913 3.596 4.430 2.437 3.548 
Safe Grey Safe Grey Safe Safe Grey Safe 

2006 2.968 1.737 5.776 2.563 3.569 5.104 2.036 3.393 
Grey Distress Safe Grey Safe Safe Grey Grey 

2007 3.152 2.397 6.264 3.182 4.544 4.916 2.259 3.816 
Safe Grey Safe Safe Safe Safe Grey Safe 

2008 4.009 3.209 6.332 3.527 5.964 4.923 2.953 4.417 
Safe Safe Safe Safe Safe Safe Grey Safe 

2009 4.043 3.512 8.488 4.666 6.693 5.376 4.734 5.359 
Safe Safe Safe Safe Safe Safe Safe Safe 

2010 5.305 5.392 12.726 6.670 10.404 11.701 11.201 9.057 
Safe Safe Safe Safe Safe Safe Safe Safe 

2011 4.695 5.636 6.806 4.830 9.608 5.976 5.856 6.201 
Safe Safe Safe Safe Safe Safe Safe Safe 

2012 4.245 5.829 4.710 5.057 7.215 4.876 4.430 5.195 
Safe Safe Safe Safe Safe Safe Safe Safe 

2013 4.444 7.478 4.730 5.642 7.627 4.762 5.351 5.719 
Safe Safe Safe Safe Safe Safe Safe Safe 

2014 5.507 8.061 6.291 6.761 9.056 5.856 5.129 6.666 
Safe Safe Safe Safe Safe Safe Safe Safe 

2015 7.155 

 

9.791 

 

6.910 6.854 9.336 8.049 5.187 7.612 
Safe Safe Safe Safe Safe Safe Safe Safe 

2016 5.598 4.710 7.227 6.090 7.217 5.992 3.164 5.714 
Safe Safe Safe Safe Safe Safe Safe Safe 

2017 5.194 4.012 5.250 5.907 7.247 5.896 3.242 5.250 
Safe Safe Safe Safe Safe Safe Safe Safe 

2018 4.582 4.032 5.730 5.098 6.817 5.869 2.931 5.008 
Safe Safe Safe Safe Safe Safe Grey Safe 

2019 3.341 8.701 5.836 2.345 6.685 6.925 3.402 5.319 
Safe Safe Safe Safe Safe Safe Safe Safe 

Average 4.086 4.466 6.720 4.787 5.967 5.499 6.258 5.398 
Safe Safe Safe Safe Safe Safe Safe Safe 

Source: Derived from Derived from Annual Reports (1996-2019) and DSE Reports 

 

     

 

 

 



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    Table 3. Average Values of Z Score and its Components 

Source: Derived from Annual Reports (1996-2019) 

Year WC/TA RE/TA EBIT/TA MV(E)/BV(TL) TS/TA Z score 

Domestic Companies 

1996 0.618 0.094 0.082 3.620 0.989 4.304 

1997 0.634 0.127 0.089 5.034 0.961 5.213 

1998 0.610 0.142 0.105 3.173 1.062 4.242 

1999 0.566 0.171 0.107 1.637 1.007 3.259 

2000 0.595 0.192 0.113 1.861 1.007 3.481 

2001 0.604 0.202 0.109 1.316 0.955 3.112 

2002 0.605 0.211 0.089 0.938 0.941 2.819 

2003 0.587 0.198 0.089 0.825 0.977 2.747 

2004 0.605 0.186 0.081 1.690 0.948 3.215 

2005 0.631 0.191 0.089 1.395 1.111 3.265 

2006 0.640 0.200 0.085 1.039 1.027 2.980 

2007 0.634 0.207 0.092 1.538 1.029 3.306 

2008 0.628 0.216 0.085 2.218 1.181 3.850 

2009 0.589 0.229 0.127 3.091 1.102 4.404 

2010 0.585 0.283 0.122 10.970 0.946 9.029 

2011 0.580 0.307 0.117 3.595 0.902 4.571 

2012 0.592 0.308 0.116 3.699 0.954 4.696 

2013 0.580 0.317 0.117 3.978 0.935 4.846 

2014 0.583 0.318 0.106 6.077 0.913 6.053 

2015 0.540 0.316 0.107 7.279 0.875 6.685 

2016 0.618 0.278 0.079 2.683 0.632 0.618 

2017 0.562 0.287 0.071 2.535 0.657 0.562 

2018 0.600 0.284 0.063 1.921 0.673 0.600 

2019 0.632 0.293 0.063 2.193 0.687 0.632 

Avg 0.601 0.231 0.096 3.096 0.936 4.155 

MNCs 

1996 0.588 0.402 0.176 12.848 1.341 11.507 

1997 0.577 0.373 0.174 10.865 1.309 9.911 

1998 0.559 0.360 0.179 4.387 1.269 5.822 

1999 0.558 0.404 0.177 3.275 1.212 5.143 

2000 0.561 0.452 0.237 4.278 1.422 6.247 

2001 0.584 0.431 0.188 3.366 1.331 5.515 

2002 0.580 0.412 0.138 3.081 1.287 4.995 

2003 0.546 0.401 0.149 2.581 1.291 4.668 

2004 0.550 0.396 0.136 3.254 1.299 5.103 

2005 0.586 0.390 0.124 2.919 1.353 4.887 

2006 0.614 0.415 0.157 2.479 1.559 4.937 

2007 0.654 0.415 0.178 2.928 1.513 5.314 

2008 0.700 0.426 0.203 3.592 1.495 5.896 

2009 0.706 0.455 0.256 4.727 1.574 6.917 

2010 0.718 0.510 0.309 10.058 1.606 10.647 

2011 0.702 0.470 0.233 5.848 1.656 7.840 

2012 0.705 0.479 0.228 4.196 1.669 6.846 

2013 0.714 0.494 0.244 4.574 1.528 7.232 

2014 0.714 0.421 0.238 6.122 1.559 8.209 

2015 0.689 0.424 0.262 8.850 1.572 9.168 

2016 0.705 0.386 0.239 5.876 1.346 7.047 

2017 0.689 0.384 0.232 5.175 1.337 6.573 

2018 0.685 0.376 0.229 4.820 1.276 6.272 

2019 0.628 0.400 0.211 5.499 1.159 6.467 

Avg 0.638 0.420 0.204 5.595 1.415 6.799 

 



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Annual reports are used to calculate the average values of the above five explanatory 

variables each year (1996-2019). The very last column of Table 3 displays the overall Z score of 

domestic firms and MNCs each year, using the mean of explanatory variable values of the seven 

corporations. The mean values of the explanatory variables of seven local firms per year are 

displayed in each row of each column of Table 3, and the Z score is calculated applying the 

formula. The very last row represents the grand mean of the average scores for all 24 years. The 

model for local companies is like as follows after placing the final mean values: 
 

𝑍 4.155 = (1.2 × 0.601) + (1.4×0.231) + (3.3×0.096) + (0.6×3.096) + (1×0.936) …………(1) 

  

 
 

The model of MNCs becomes the following after placing the grand mean value: 

𝑍 6.799 = (1.2 × 0.638) + (1.4×0.420) + (3.3×0.204) + (0.6×5.595) + (1×1.415) ………...(2) 

 

  
 

 It is observed from the models (1) and (2) that the shift in MV(E)/BV(TL) triggered 

the Z score to vary.The shift in Z-score is observed by adjusting MV(E)/BV(TL) by 1 percent, 5 

percent and 10 percent, and it is evident in Table 4 that the increase in MV(E)/BV(TL) results in 

an increase in Z-score and vice versa. The rate of Z-score increase and decrease is the same for a 

given percentage change in MV (E)/BV (TL). For domestic industries, if MV (E)/BV (TL) 

increases by 1 percent, the Z score increases by 0.4 percent or 0.02 while 5 percent and 10 

percent increase leads to an increase of 2.2 percent or 0.09 and a Z score rise of 4.5 percent or 

0.195, respectively. From table 4, it is obvious that 1 percent increase in MV(E)/BV(TL) leads to 

0.49 percent or 0.033 increase in Z-score in the case of MNCs, while 5 percent and 10 percent 

increase lead to 2.47 percent or 0.168 increase and 4.94 percent or rise in Z-score respectively. 

      Table 4. Impact of MV(E) / BV(TL) on Z Score of Companies 

% change in MV 

(E) / BV (TL) 

New MV (E) 

/ BV (TL) 

New 

Z-score 

Existing 

Z-score 

change in 

Z-score 

% change 

in Z-score 

Domestic Companies 

Increase by 1% 3.12696 4.174 4.155 0.019 0.4 

Decrease by 1% 3.06504 4.136 4.155 -0.019       -0.4 

Increase by 5% 3.2508 4.248 4.155       0.093 2.2 

Decrease by 5% 2.9412 4.062 4.155 -0.093       -2.2 

Increase by 10% 3.4056 4.341 4.155 0.186 4.47 

Decrease by 10% 2.7864 3.969 4.155 -0.186      -4.47 

MNCs 

Increase by 1% 5.65095 6.832 6.799 0.033 0.485 

Decrease by 1% 5.53905 6.765 6.799 -0.034 -0.500 

Increase by 5% 5.87475 6.967 6.799 0.168 2.471 

Decrease by 5% 5.31525 6.631 6.799 -0.168 -2.471 

Increase by 10% 6.1545 7.135 6.799 0.336 4.941 

Decrease by 10% 5.0355 6.463 6.799 -0.336 -4.941 

         Source: Derived from Z Score Model                Note: Compiled by the researcher 

WC/TA EBIT/TA MV(E)/BV(TL)

) 
RE/TA TS/TA 

WC/TA EBIT/TA MV(E)/BV(TL) RE/TA TS/TA 



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Currently, each company's gross borrowing is valued at variations of 1 percent, 5 percent 

and 10 percent each year, and a new ratio of Equity/Book Value of Total Liabilities is calculated. 

The annual average of this ratio at that point is determined by taking the annual average of seven 

local companies. The current average of this ratio is then assessed by taking into account the 

mean of the average value for all twenty four years. The shift in debt increases the total 

liabilities' book value, which ultimately changes the ration of the MV(E)/BV (TL) in the opposite 

direction. In the Z score equation, a new value is placed to evaluate the impact of the leverage 

shift on the Z score. The average TD in thecase of local firms is Tk.5097.92 million, representing 

55.20% of TL(Average TL is Tk. 9233.76 million). The average TD in the case of MNCs is Tk. 

332.42 million, representing 13.67 percent of TL (Average TL is Tk. 2431.45 million). The 

effect of the shift in leverage on the Z score is shown in Table 5.  

Table 5. Impact of Debt on Z score 

 

% change in TD % change 

in TL 

New MV 

(E) / BV 

(TL) 

New 

Z-score 

Existing 

Z-score 

change 

in Z-

score 

% 

change 

in Z-

score 

Domestic Companies 

Increase by 1% 0.552% 3.0790 4.145 4.155 -0.010 -0.240 

Decrease by 1% -0.552% 3.1131 4.165 4.155 0.010  0.240 

Increase by 5% 2.76% 3.0128 4.105 4.155 -0.050     -1.20 

Decrease by 5% -2.76% 3.1838 4.208 4.155 0.053 1.27 

Increase by 10% 5.52% 2.9340 4.058 4.155 -0.097     -2.33 

Decrease by 10% -5.52% 3.2768 4.263 4.155 0.108 2.59 

MNCs 

Increase by 1% 0.1367 5.587 6.794 6.799 -0.005 -0.073 

Decrease by 1% -0.1367 5.602 6.803 6.799 0.004 0.058 

Increase by 5% 0.6835 5.557 6.776 6.799 -0.023 -0.338 

Decrease by 5% -0.6835 5.633 6.822 6.799 0.023 0.338 

Increase by 10% 1.367 5.519 6.754 6.799 -0.045 -0.661 

Decrease by 10% -1.367 5.672 6.845 6.799 0.046 0.676 

Source: Derived from Z Score Model                       Note: Compiled by the researcher 

 

Table 5 shows that the Z score and total debt have an inverse relationship, which implies 

that decline of debt would enhance Z score and vice versa. For local companies, Z decreases by 

0.01 or 0.24 percent if the overall debt rises by 1 percent. A 10 percent increase in total debt 

decreases Z score by 0.097 or 2.33 percent while 10 percent rise in debt decreases Z score by 

0.108 or 2.59 percent. With MNCs, for a 1 percent increase in overall debt, the Z score decreases 

by 0.005 or 0.073 percent. A 10% increase in total debt reduces the Z score by 0.045 or 0.661 

percent, while a 10% decrease in total debt raises the Z score by 0.046 or 0.676 percent. 

Comparison of Effect of Leverage on Z Score 

Table 5 revealed that the Z score's responsiveness to debt adjustment is higher for local 

companies as the change and the percent change in local companies' Z score is greater than that 

of MNCs.The Z score of local companies increases by 0.01 if debt shifts by 1% and the Z score 

shifts by 0.005 for equal shifts in debt of MNCs. Similarly, in the context of local companies, the 



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39 

10 percent decline in debt enhances Z score by 0.108 or 2.59 percent, while the MNC's Z score 

enhances by 0.046 or 0.676 percent for the similar amount of debt decline.  

Test of Hypothesis 

Independent sample t-test have been applied to test the null hypothesis. The test is used to 

evaluate the significance of variation in Z score between domestic companies and MNCs. The 

following table shows the result of t-test. From the table it is seen that in both assumptions of 

variances, the p-value of t-statistic is 0.000 which is less than 0.01. It indicates that the null 

hypothesis is rejected which means that there is significant difference between Z scores of 

domestic firms and MNCs.  

Table 6. Independent Samples t-Test 

 

 Levene's 

Test for 

Equality 

of 

Variances 

t-test for Equality of Means 

F Sig. t df Sig. 

(2-

tailed) 

Mean 

Difference 

Std. Error 

Difference 

95% C.I. of the 

Difference 

Lower Upper 

Z 

score  

Equal 

variances 

assumed 

.000 .990 -3.63 334 .000
* 

-1.2421 .3420 - 

1.9150 

- 

.5692 

Equal 

variances 

not assumed 

  -3.63 325 .000
* 

-1.2421 .3420 - 

1.9151 

- 

.5691 

Source: Authors’ own computation, Note: *significant at 1% level 

 

RECOMMENDATIONS AND CONCLUSION 

Liability management is an important arena of corporate finance and it is critical for a 

manufacturing firm because financial distress, financial risk, solvency, and long run survival of a 

firm depends on it. By comparing 168 firm-year observations for each group it is seen that in 

case of domestic companies there are 8 observations in distress zone, 64 in grey zone and 96 in 

safe zone while in case of MNCs the figures are 3, 15 and 150 respectively. Yearly Z scores of 

MNCs are significantly greater compared to that of domestic firms. From observing mean Z 

score of individual firms, it is seen that five domestic firms are in safe zone but two are in grey 

zone while in case of MNCs all firms are in safe zone. These findings are supported by the fact 

that market value of equity is more than 3 times of liability in case of domestic companies while 

it is more than 5 times of liability in case of MNCs. Domestic companies can increase their Z 

score by reducing debt in their capital structure. Besides it, by observing individual components 

of Z score it can be said that domestic companies basic earning power (EBIT/TA), retained 

earnings to asset (RE/TA) and asset utilization (TS/TA) ratio are much lower than MNCs and Z 

score can be enhanced by improving these ratios. Further studies can be conducted on non-

manufacturing companies to assess financial distress by applying different models and different 

industrial sectors can be compared with each other.  

 

 



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40 

ACKNOWLEDGEMENTS 

The authors are indebted to managers of sample firms at different levels as well as to authority of 

the Dhaka Stock Exchange of Bangladesh for providing secondary data and reports.  

 

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APPENDICES 

 

Appendix A: Name of the Firms 

 

The Names of the Domestic Companies are:  

 

Aftab Automobiles Ltd.(AAL), Agricultural Marketing Company Ltd. (AMCL), Beximco 

Pharmaceuticals Ltd.(BPL), Square Pharmaceuticals Ltd.(SPL), Apex Footwear Ltd.(AFL), 

Confidence Cement Ltd.(CCL), and Padma Oil Company Ltd.(POC) 

 

The Names of the MNCs are:  

 

Singer Bangladesh Ltd.(SBD), British American Tobacco Bangladesh Company Ltd.(BATB), 

GlaxoSmithKline Bangladesh Ltd.(GSK), Reckitt Benckiser (Bangladesh) Ltd.(RBB), Bata Shoe 

Company Ltd.(BSC), Heidelberg Cement Bangladesh Ltd.(HCL), and Linde Bangladesh 

Ltd.(LBD) 

 

Appendix B: Acronyms 

 

MV = Market Value 

TL = Total Liability 

TA = Total Assets 

WC = Working Capital 

BV = Book Value 

TS = Total Sales 



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RE = Retained Earnings 

TD = Total Debt 

 

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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 (http://creativecommons.org/licenses/by/4.0/) 


