E.I Altman’s Z score
1.1
1.2
1.3
1.4
1.5
1
Origin of the report.
Objective of the report.
Methodology of the report.
Sources of data.
Limitation of the study.
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E.I Altman’s Z score
1.1 Origin of the Report
Bank is an intermediary institution that makes relationship between the owner of surplus
savings and the investor of deficit capital. This study focuses on prediction of financial health
of banking industry in Bangladesh. Looking into the scenario of business today the enhancing
uncertainty scenario takes away the surety of existence from firms. Perhaps to be sure of the
longevity of the firm becomes the prime issue of concern by all the business houses. The
viability of banks holds prime importance as it relates to financial investments, funding,
capacity building and expansion by ploughing back profits. Z score has been used as a tool to
evaluate the credibility of the firms. The financial crisis of 2007-08 has refocused the attention
on measurement of financial health, bankruptcy and liquidity risk of the banks. Z-score is a
popular measure for predicting probability of bank’s bankruptcy. The Z-score can be calculated
using only accounting information in contrast to market-based risk measures. This paper
provides the Z score value for the private commercial banks. This value is useful when these
banks demand loans from the Bangladesh Bank or any other funding agency. The usage of
back propagation neural network is to forecast the internal parameters of Z score and then use
these internal parameters to forecast the Z score value up to 2020. The research focus on six
banks that are considered in the conventional and Islamic shariah based norms being Dhaka
Bank Limited, Prime Bank Limited, Pubali Bank Limited, Uttara Bank Limited, Shahjalal
Islami Bank Limited and Social Islami Bank Limited.
The paper is divided into sections. Section I gives the introduction about the research done in
the paper, Section II discusses the bankruptcy scenario related to Z score, Section III describes
the model design and methodology, Section IV discusses the BPNN Model application in Z
Score for Public Sector banks in India, Section V evaluates the predictions of internal
parameters of Z score, Section VI brings forth the findings and the last Section VII sums up
the research.
1.2 Literature Review
Several researches have been conducted in the area. O’Leary argues that prediction of
bankruptcy probably is one of the most important business decision-making problems affecting
the entire life span of a business, failure results in a high cost from the collaborators (firms and
organizations), the society and the country’s economy. Over the last 35 years, the topic of
company failure prediction has developed to a major research domain in corporate finance.
Academic researchers from all over the world have been developing a gigantic number of
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E.I Altman’s Z score
corporate failure prediction models, based on various types of modeling techniques. Besides
the classic cross-sectional statistical methods, which have produced numerous failure
prediction models, researchers have also been using several alternative methods for analyzing
and predicting business failure (Lahiri, 2013).
To date, a clear overview and discussion of the application of alternative methods in corporate
failure prediction is still lacking. Research has shown that most business failure is caused by
bad or poor management. This could be in the form of inexperienced management styles, fraud,
and rapid technological changes amongst other variables. Financial failure may take the form
of bankruptcy or insolvency (Khaliq et.al. 2014).
Bankruptcy refers to a condition where the total liabilities exceed the fair value of assets.
Financial statements are normally used to gauge the performance of the firm and its
management. The financial statements commonly used are profit and loss statement, balance
sheet and cash flow statements. From the financial statements, various ratios can be calculated
to assess the current performance future prospects of the concerned firm. Some of the ratios
used include current ratio, quick ratio, and working capital to total debt, total debt to total
assets, profit margin to sales and return on total assets. Perhaps the best way to avoid failure is
to examine the myriad explanations for business failure. Studies carried out by Altman used
financial ratios to predict occurrence of bankruptcy and he was able to predict 94% correctly
one year before bankruptcy occurred and 72% two years before its actual occurrence.
Dimitras, Koksal, and Kale (2014) pointed out that after 30 years of research on this topic.
There is no generally accepted model for business failure prediction that has its basis in a causal
specification of underlying economic determinants. Because of the confusingly varied and
restrictive assumptions underlying these classic statistical models, there is need to recourse to
alternative methods. Prior empirical studies of failure have concentrated almost exclusively on
financial ratio data, though other studies of failure usually cite managerial variables as being
critical. The usefulness of ratio-based business failure prediction models has been questioned.
Mahtab (2015) has done a financial analysis of Lafarge Surma Cement in Bangladesh using
Altman’s Z-scoring model. Abdullah (2015) has made an empirical analysis of liquidity,
profitability and solvency of Bangladeshi banks and found that 7 banks are in healthy financial
position and 22 banks are insolvent during the time period of FY 2009-2014 as well as Islamic
or Sariah follower banks are doing better than conventional banks. He has also mentioned that
sate owned banks are doing better than before.
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E.I Altman’s Z score
Mostofa et.al. (2016) have predicted the financial distress of private sector banking industry of
Bangladesh using Z score model of Altman and concluded that this model was found to be 72%
accurate in predicting bankruptcy two years before the event. All the above literature has been
made either from the viewpoint of manufacturing firm or banks. But research on comparison
of financial health prediction between state owned banks and private banks in Bangladesh are
not available and for this reason this paper has taken an attempt to predict the financial health
of banking sector of Bangladesh using Altman’s Z score as well as compare between state
owned banks and private banks so that a clear picture about the two banking sectors can be
depicted. This will give an insight for the bankers which will help them for taking necessary
managerial actions for their better financial performance.
1.3 Objectives of the Report:
The objectives of the research are as follows:

To appraise the financial performance of sampled private sector banks.

To know the stability and profitability of the selected banks.

To predict the financial health and soundness of selected private sector banks

To carry out comparative analysis of the financial performance of the sampled banks.

To make comparison among Z-Scores of the State-Owned Commercial Banks.
1.4 Methodology of the Report:
Universe:
The Bangladeshi banking industry of 66 scheduled and nonscheduled banks as a whole
irrespective of its ownership viz. state-owned, private, foreign or cooperative, is the universe
of the study.
Sample
The researcher has drawn the following six leading private sector banks as the sample of the
research study from the above universe:
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E.I Altman’s Z score
1.Dhaka Bank
Limited
1.Social
Islami Bank
Limited
1.Prime
Bank Limited
Shahjalal
Islami Bank
Limited
1.Pubali
Bank Limited
Uttara Bank
Limited
Conventional PCBs
1. Dhaka Bank Limited,
2. Prime Bank Limited,
3. Pubali Bank Limited,
4. Uttara Bank Limited,
Islami Shariah Based PCBs
5. Shahjalal Islami Bank Limited and
6. Social Islami Bank Limited
1.5 Source of the Data
The study is based on quantitative data which is collected from annual audited financial
statements of the sample banks from both public and private sectors. The data type which is
used in this research is a quantitative one. Different articles and websites of the selected sample
banks are used as secondary source of data.
1.6 Limitations of the Report:
1. The study is limited to six banks only so, it may not represent whole of the universe.
2. The secondary data has been sourced for the research study so; the limitations of the
secondary data, limit the accuracy and authenticity of the conclusion.
3. The study is based on only Altman Z-score Model which may not draw the true
conclusion.
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2.1 Uttara Bank Limited
2.2 Pubali Bank Limited
2.3 Prime Bank Limited
2.4 Social Islami Bank Limited
2.5 Dhaka Bank Limited
2.6 Shahjalal Islami Bank Limited
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1.1 Uttara Bank Limited
Uttara Bank Limited celebrated 50th anniversary of its Banking Service in 2015. This well
established and ancient bank has a rich history. With the initiation of some renown Bengali
businessmen it was established to facilitate the disadvantaged people of the then East Pakistan
and started its banking operation officially on 28th January of 1965 in name of “Eastern
Banking Corporation” with four branches which soon reached 60 just before the independence.
During Non-cooperation movement in 1971, this bank performed the treasury function of East
Bengal. At present the bank has 239 branches and all are under online network. In addition, its
effective and diversified approach to seize the market opportunities is going on as continuous
process to accommodate new customers by developing and expanding rural, SME financing
and offshore banking facilities. Besides these traditional delivery points, the bank is also very
active in the alternative delivery area. It currently has the facilities of SMS Banking, Internet
Banking and a large number of ATMs of its own with ATM sharing arrangement with other
partner banks.
The Bank is proud of its management team headed by Managing Director, Mr. Mohammed
Rabiul Hossain and it encourages all employees to devote a measure of their time and talent to
support distressed community by participating in CSR programs. Its main objective is to render
service to the people whether rich or poor and to contribute to the development of the nation.
1.2 Pubali Bank Ltd.
The Bank was initially emerged in the Banking scenario of the then East Pakistan as Eastern
Mercantile Bank Limited at the initiative of some Bangalee entrepreneurs in the year 1959
under Bank Companies Act 1913 for providing credit to the Bangalee entrepreneurs who had
limited access to the credit in those days from other financial institutions. After independence
of Bangladesh in 1972 this Bank was nationalized as per policy of the Government and
renamed as Pubali Bank. Subsequently due to changed circumstances this Bank was
denationalized in the year 1983 as a private bank and renamed as Pubali Bank Limited. Since
inception this Bank has been playing a vital role in socio-economic, industrial and agricultural
development as well as in the overall economic development of the country through savings
mobilization and investment of funds.
At Present, Pubali Bank is the largest private commercial bank having 488 branches and it has
the largest real time centralized online banking network.
1.3 Prime Bank ltd.
Prime Bank, is a top-tier second generation local commercial bank in Bangladesh established
in 1995. Headquartered in the heart of Dhaka's bustling financial hub Motijheel, the Bank's
operational footprint is spread all over the country with 146 branches and 170 ATM locations.
It was incorporated under the Companies Act of 1994. Prime Bank is best known for its
expertise in Corporate and Institutional Banking and its innovative Digital Banking services.
Global Finance, a North America based leading financial publication has recognized Prime
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E.I Altman’s Z score
Bank as the Best Bank in Bangladesh in 2020. Prime Bank has also been awarded as the Best
Digital Bank in Bangladesh in 2020 by Asia money, another global financial publication. In
2014, Prime Bank initiated a 'Business Model Restructuring and Centralization' project to bring
more coherence in its banking practices while re-engineering its business processes to enhance
resource efficiency.
1.4 Social Islami Bank Ltd.
The SOCIAL ISLAMI BANK LTD (SIBL), a second-generation bank, operating since 22
November, 1995 based on Shariah Principles, has now 86 branches all over the country with
two subsidiary companies - SIBL Securities Ltd. & SIBL Investment Ltd. Targeting poverty,
SOCIAL ISLAMI BANK LTD. is indeed a concept of 21st century participatory three sector
banking model in one. in the formal sector, it works as an Islamic participatory Commercial
Bank with human face approach to credit and banking on the profit and loss sharing: it is a
Non-formal banking with informal finance and credit package that empowers and humanizes
real poor family and create local income opportunities and discourages internal migration; it is
a Development Bank intended to monetize the voluntary sector and management of Waqf,
Mosque properties and introducing cash Waqf system for the first time in the history. In the
formal corporate sector, this Bank would, among others, offer the most up to date banking
services through opening of various types of deposit and investment accounts, financing trade,
providing letters of guarantee, opening letters of credit, collection of bills, leasing of equipment
and consumers' durable, hire purchase and instalment sale for capital goods, investment in lowcost housing and management of real estates, participatory investment in various industrial,
agricultural, transport, educational and health projects and so on.
1.5 Dhaka Bank Limited
Dhaka Bank Limited (DBL) is the leading private sector bank in Bangladesh offering full range
of Personal, Corporate, International Trade, Foreign Exchange, Lease Finance and Capital
Market Services. The Bank has launched Online Banking service and being fully equipped with
industry standard IT infrastructure, Online Banking, E-Commerce, Internet Banking (iBank)
and SMS Banking – Dhaka Bank is one of the fastest growing private banks in Bangladesh.
Dhaka Bank Limited is the preferred choice in banking for friendly and personalized services,
cutting edge technology, tailored solutions for business needs, global reach in trade and
commerce and high yield on investments, assuring Excellence in Banking Services.
Market Services (CMS).As envisaged in the Memorandum of Association and as licensed by
Bangladesh Bank under the provisions of the Banking Companies Act 1991, the Company
started its banking operation and entitled to carry out the following types of banking business:
(i) All types of commercial banking activities including Money Market operations.
(ii) Investment in Merchant Banking activities.
(iii) Investment in Company activities.
(iv) Financiers, Promoters, Capitalists etc.
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(v) Financial Intermediary Services.
1.6 Shahjalal Islami Bank Limited
Shahjalal Islami Bank Limited, a Shariah Based Commercial Bank in Bangladesh was
incorporated as a Public limited company on 1st April, 2001 under Companies Act 1994. The
Bank commenced commercial operation on 10th May 2001 by opening its 1st branch, i.e.
Dhaka Main Branch at 58, Dilkusha, Dhaka obtaining the license from Bangladesh Bank, the
Central Bank of Bangladesh. Its Head Office is situated at 2/B, Uday Sanz, Gulshan South
Avenue, Gulshan-1, Dhaka1212, Bangladesh. The Bank so far opened 2 (two) branches in
2001, 6 (six) branches in 2002, 2 (two) branches in 2003, 2 (two) branches in 2004, 4 (four)
branches in 2005, 5 (Five) Branches in 2006, 5 (Five) Branches in 2007, 7 (Seven) Branches
in 2008, 18 (Eighteen) Branches in 2009, 12 (Twelve) Branches in 2010 and 10 Branches in
2011. Total number of branches stood at 79 (Seventy nine). Besides this, the bank is working
to expand its business by opening more 11 (eleven) branches in Dhaka and some other
important business location of the country in the year 2012 for which we have already taken
approval from the Bangladesh Bank.
Vision of SJIBL:
To be the unique modern Islamic Bank in Bangladesh and to make significant contribution to
the national economy and enhance customers’ trust & wealth, quality investment, employees’
value and rapid growth in shareholders’ equity.
Mission of SJIBL:







To provide quality services to customers.
To set high standards of integrity.
To make quality investment.
To ensure sustainable growth in business
To ensure maximization of Shareholders’ wealth.
To extend our customers innovative services acquiring state-of-the-art technology
blended with Islamic principles.
To ensure human resource development to meet the challenges of the time.
Objectives of SJIBL:








To conduct interest free banking.
To establish participatory banking instead of banking on debtor-creditor relationship.
To invest through different modes permitted under Islamic Shariah.
To accept deposits on profit-loss sharing basis.
To establish a welfare-oriented banking system.
To extend co-operation to the poor, the helpless and the low-income group for their
economic uplift.
To pay a vital role in human development and employment generation.
To contribute towards balanced growth and development of the country through
investment operations particularly in the less developed area.
Principal Activities:
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The principal activities of the Bank is to provide all kinds of commercial banking products and
services to the customers including deposits taking, cash withdrawal, extending investments to
corporate organization, retail and small & medium enterprises, trade financing, project finance,
working capital finance, lease and hire purchase financing, issuance of Debit Card. Its vision
is to be the best private commercial bank in Bangladesh in terms of efficiency, capital
adequacy, asset quality, sound management and profitability.
Strategic plan for future growth
The Banking industry experienced intensification of competitive pressure as the national and
international banks operating in Bangladesh strongly pursued the banking and financing needs
of the Corporate, Retail, SME sector customers through diversification of products and services
and extending automated banking service with ATM, Debit card facilities and Internet
Banking. Besides, rates of profit became very competitive for deposit and lending; Customers
are demanding higher rate of return against their deposits, on the other hand asking the banks
to reduce their lending rates.
Considering the overall scenario, SJIBL continues to focus on its delivery channel, technology,
Human Resource and its brands along with branch network, Business promotion, Corporate
Social Responsibility and product diversification.
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3.1 Z-Score
3.2 Z-Scores process
3.3 Criticisms of Z-Scores
3.4 Instrumentation
3.5 Zones of Discriminations
3.6 Ranking of Private commercial banks
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E.I Altman’s Z score
3.1 Z-Score
A Z-score is a numerical measurement that describes a value's relationship to the mean of a
group of values. Z-score is measured in terms of standard deviations from the mean. If a Zscore is 0, it indicates that the data point's score is identical to the mean score. A Z-score of 1.0
would indicate a value that is one standard deviation from the mean. Z-scores may be positive
or negative, with a positive value indicating the score is above the mean and a negative score
indicating it is below the mean. Z-score compares a bank's buffers (capitalization and returns)
with the volatility of those returns. It captures the probability of default of a country's banking
system, calculated as a weighted average of the z-scores of a country's individual banks (the
weights are based on the individual banks' total assets).
3.2 Z-Scores process
Z-scores reveal to statisticians and traders whether a score is typical for a specified data set or
if it is atypical. Z-scores also make it possible for analysts to adapt scores from various data
sets to make scores that can be compared to one another more accurately.
Edward Altman, a professor at New York University, developed and introduced the Z-score
formula in the late 1960s as a solution to the time-consuming and somewhat confusing process
investors had to undergo to determine how close to bankruptcy a company was.1 2 In reality,
the Z-score formula that Altman developed actually ended up providing investors with an idea
of the overall financial health of a company.
Over the years, Altman continued to reevaluate his Z-score. From 1969 until 1975, Altman
looked at 86 companies in distress. From 1976 to 1995, he observed 110 companies. Finally,
from 1997 to 1999, he evaluated an additional 120 companies. From his findings, it was
revealed that the Z-score had an accuracy of between 82% and 94%.3
A Z-score is the output of a credit-strength test that helps gauge the likelihood of bankruptcy
for a publicly traded company. The Z-score is based on five key financial ratios that can be
found and calculated from a company's annual. The calculation used to determine the Altman
Z-score is as follows:4
ζ=1.2A+1.4B+3.3C+0.6D+1.0E
Where:
(ζ)=The Altman Z-score
A=Working capital/total assets
B=Retained earnings/total assets
C=Earnings before interest and taxes (EBIT)/total assets
D=Market value of equity/book value of total liabilities
E=Sales/total assets.
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E.I Altman’s Z score
Typically, a score below 1.8 indicates that a company is likely heading for bankruptcy.
Conversely, companies that score above 3 are less likely to experience bankruptcy
3.3 Criticisms of Z-Scores
The Z-score should be calculated and interpreted with care. Since companies in trouble may
sometimes misrepresent or cover up their financials, the Z-score is only as accurate as the data
that goes into it.
Additionally, the Z-score isn't very effective for new companies with little to zero earnings.
Regardless of their actual financial health, these companies will score low. Moreover, the Zscore doesn't address the cash flows of a company. Rather, it only hints at it through the use of
the net working capital-to-asset ratio.
Finally, Z-scores can swing from quarter to quarter if a company records one-time write-offs.
These events can change the final score and may falsely suggest a company is on the brink of
bankruptcy.
3.4 Instrumentation
This study is analytical in nature and related to the analysis of financial health or soundness of
selected banks both in public and private sectors. Altman Z score model (Altman, 1968) is used
to predict the financial health and also to compare between these two sectors. Altman's Z-score
is a customized edition of the discriminant analysis technique of R. A. Fisher (1936). For
analyzing the study various financial ratios have calculated firstly and then forecast the
financially distressed and non-distressed banks using the Z-score model. Altman Z-score model
(Altman, 1968) considers four independent variables and each of them represents the common
financial ratios, weighted by coefficients. According to Altman Z score model (Altman, 1968)
the following equation for bankruptcy or possibility of bankruptcy of the non-manufacturing
or service industry has been analyzed.
Z-Score model: Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4+ 1.0X5
X1 = (Current Assets − Current Liabilities) / Total Assets
X2 = Retained Earnings / Total Assets
X3 = Earnings before Interest and Taxes / Total Assets
X4 = Market Value of Equity / Total Liabilities
X5= Revenue / Total Asset
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3.5 Zones of Discriminations:
Scenario
Score
Zone or indicator
Description
1.
Z=>2.6
“Safe”
The bank is financially
sound and there is a
least possibility that the
bank will face financial
distress. It can be said
that
the
bank
is
financially healthy.
1.1 ≤ Z ≤ 2.6
2.
“Grey”
The bank falls in the
gray area that means
there is less possibility
that the bank will face
financial distress in the
near future.
Z< 1.1
3.
“Distress”
There is a possibility
that the bank will face
financial distress even
bankruptcy in the near
future. It can be said
that the bank is in
vulnerable position
3.6 Ranking of Private commercial banks
Name of Bank
Rank based on
Liquidity
Dhaka
Bank 2
Z –score
Profitability
5
Limited
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E.I Altman’s Z score
Bank 4
6
4
Bank 5
1
5
Bank 1
2
1
Islami 6
3
6
4
3
Prime
Limited
Pubali
Limited
Uttara
Limited
Shahjalal
Bank Limited
Social Islami Bank 3
Limited
A lot of studies have done on financial distress using the Altman Z-score bankruptcy model in
the global context as well as in Bangladesh. Moreover, in Bangladesh, most of the researches
about financial distress are done based on PCBs. From literature review, it is observed that one
researcher found that the SOCBs in Bangladesh are financially distressed and are characterized
by low capital adequacy ratio, high loan loss provision, liquidity problem, poor earning quality,
and management inefficiency (Jahan, 2018). However, another researcher has tried to predict
the financial health of banking industry in Bangladesh applying the Altman’s Z-score model
and made a comparison of Z-score between the SOCBs and PCBs. The analysis reveals that
the PCBs possess better financial health than their counter parts (Parvin, et al., 2016). Hence,
there is a scope of further research. Prediction of the financial distress of PCBs is really
necessary as performance of the PCBs in Bangladesh is deteriorating day by day. Therefore,
the outcomes of this study will keep a significant contribution in literature and help the
regulators to formulate policy and its implementation.
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Liquidity Ratio
1
0,5
0
-0,5
Dhaka Bank
Limited
Prime Bank
Limited
Pubali Bank
Limited
Uttara Bank
Limited
Shahjalal Islami Social Islami Bank
Bank Limited and
Limited
-1
2016
2017
2018
2019
0,8
0,6
0,4
0,2
0
-0,2
-0,4
-0,6
-0,8
2020
Profitability
Profitability
0,04
0,02
0
0
1
2
3
4
5
6
7
Year
2016
2017
2018
2019
2020
The average liquidity ratio (working capital to total assets) of the Uttara Bank Limited was the
highest at 0.102 and the lowest for the Shahjalal Islami Bank Limited at -0.723962873. The
average EBIT ratio was negative for the Shahjalal bank and positive for the remaining others.
Moreover, the earnings ratio was the lowest for the Shahjalal Islami Bank Limited -7.239%
and the highest for the Pubali Bank Limited at 1.99%. On the other hand, the average retained
earnings ratio was maximum in case of Uttara Bank Limited at 1.9% and minimum for the
Dhaka Bank Limited at -0.80%. Poor working capital to total assets ratio (liquidity ratio), and
Z-score of the Shahjalal Islami Bank Limited put it on the last (6th) position. However,
Liquidity and Z-score of the Uttara Bank placed it the first position (Table 2). Details are shown
in Appendix 1.
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4.1 Comparison of Z-Score in different time
interval
4.2 Comparison of Z-Score between
commercial banks and Islamic Banks
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E.I Altman’s Z score
4.1 Comparison of Z-Score in different time interval
Detail calculation of Z-Score and percentage change in Z-score is shown in Appendix. Here is
a table with the Z-Score of second quarter of 2020 and a graph to show the percentage change
in quarters. Table shows all the banks are lying in the red zone which is very alarming for the
banking sector as well as for the whole economy of Bangladesh. If the banking sector of
Bangladesh collapses, all the progress of the country will be stopped down as this the
dominating industry of the country. So, necessary actions should be taken by the banks and
regulator Bangladesh bank as soon as possible.
Name of Bank
Dhaka
Z Score
Rank
Distress zone
Bank 0.345675868
2
Red zone
Bank 0.19607425
4
Red zone
Bank 0.161790594
5
Red zone
Bank 0.855990609
1
Red zone
Islami -0.653674439
6
Red zone
3
Red zone
Limited
Prime
Limited
Pubali
Limited
Uttara
Limited
Shahjalal
Bank Limited and
Social Islami Bank 0.26164801
Limited
Table: Average Z-SCORE with ranks
Table 1 depicts that the Z-score of the Shahjalal Islami Bank Limited and was negative during
2016-2020. All other banks showed negative Z-score during the study period. However, in case
of Dhaka Bank Limited, the Z-score showed ups and downs throughout the period. As a result,
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the Altman Z-score model provides the best rank to the Uttara bank ltd. among the six PCBs
and though put it in red zone as it secured on an average 0.583581 score. However, others are
also found in the distress zone as they secured an average score < 1.10. Table 2 shows brief
description of the ranking of the six PCBs on the basis of the liquidity, profitability and Z-score
Name of
Bank
Dhaka Bank
Limited
Prime Bank
Limited
Pubali Bank
Limited
Uttara Bank
Limited
Shahjalal
Islami Bank
Limited and
Social
Islami Bank
Limited
2016
2017
2018
2019
2020 Distress
0.351059
0.412725
0.474967
0.239602
0.250027
zone
Red zone
0.19917
0.138806
0.153926
0.250063
0.238406
Red zone
0.172972
0.201047
0.141544
0.156842
0.136547
Red zone
0.754639
0.85941
0.871252
0.938054
0.856598
Red zone
-0.75329
-0.67814
-0.61847
-0.60054
-0.61793
Red zone
0.283284
0.250411
0.265006
0.262417
0.247121
Red zone
Table: Z-SCORE VALUE FOR SAMPLE BANKS FROM 2016-2020
Z-SCORE VALUE FOR SAMPLE BANKS
1,5
1
0,5
0
2016
2017
2018
2019
2020
-0,5
-1
Dhaka Bank Limited
Prime Bank Limited
Pubali Bank Limited
Uttara Bank Limited
Shahjalal Islami Bank Limited and
Social Islami Bank Limited
Figure: Z-SCORE VALUE FOR SAMPLE BANKS
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E.I Altman’s Z score
Table exposes that the financial health of all the private sector banks, under study falls in ‘Red’
Zone. ‘Red’ Zone means they are at high Risk. Uttara Bank has the first position in regards
with financial soundness followed by Dhaka Bank Limited, Prime Bank Limited, Pubali Bank
Limited, Uttara Bank Limited, Shahjalal Islami Bank Limited and Social Islami Bank Limited
are on the ‘Distress’ Zone ao. The financial health of SIBL is the poorest amongst all the
sampled private sector banks.
4.2 Comparison of Z-Score between commercial banks and Islamic Banks
Islamic Banks have lower Z-Score than the commercial banks. ICB Islamic bank Ltd. has
negative Z-Score. In 2020, most of the banks have lower Z-score than the 2019. Though
commercial banks have higher Z-Score than the Islamic Banks, they belong to the Red Zone.
Dhaka Bank Limited, Prime Bank Limited, Pubali Bank Limited, Uttara Bank Limited,
Shahjalal Islami Bank Limited and Social Islami Bank Limited all of the banks belongs to the
Red Zone. All the Islamic Banks belong to the Red Zone and Shahjalal Islami Bank Limited is
the only bank which has negative Z-Score. That is to indicate that the financial health of the
Islamic banks is in more disastrous form than the conventional banks.
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20
E.I Altman’s Z score
5.1 Findings
5.2 Recommendations
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21
E.I Altman’s Z score
5.1 Findings:

From the research, it can be concluded that all the sampled banks fall in ‘Red’ Zone yet
out of six selected banks four conventional banks have better financial position
compared to Shahjalal and Social Islami Banks.

All the sampled banks are required to improve their financial performance to avoid
bankruptcy. The banks can improve their financial performance or Z-score by
maintaining working capital, increasing, retained earnings and EBIT and by reducing
total debts.

It is suggested that investors and depositors should not only rely on common financial
ratios as fundamental analysis and Z-score Model but should also consider the other
factors like international affairs, political stability, economic factors etc. while taking
investment decision.

Although the Z-score can be influenced by external events, it is a useful tool to provide
a quick analysis of where a company stands compared to competitors, and for tracking
the risk of insolvency over time.
5.2 Recommendations
1. Long-term debt should be used more for financing than short-term debt. This will help
to increase non-current liabilities and decrease current liabilities. The reduction of
current liabilities will lead to the increase of liquidity ratios.
2. Banks must find out what are their unproductive assets and then they have to get rid of
these assets. Cash balances will be increased, and the liquidity ratios will be increased
too.
3. Corruption and illegal money outflow from the country can be reduced if the regulatory
authority, agencies that enforce law, business associations, tax authority and banks take
efforts. Thus, both liquidity and financial health will be improved.
4. Forecasting cash flow is an effective tool which helps to manage the cash and decide
the level of cash. This will help to manage cash effectively and thus the liquidity ratios
and financial health will be improved.
5. Proper monitoring by regulatory authority and government can be helpful to improve
the financial health and liquidity ratios of the company. Assistance from government
and Bangladesh Bank will make the banks able to recover the current crisis.
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E.I Altman’s Z score
6. Corporate efficiency and managerial efficiency are necessary for making profit and
running out any kind of business successfully. They help to improve the liquidity ratios
and financial health.
7. To overcome the effects of this pandemic and recover the loss is a challenge for every
sector. Managements of the banks must accept the challenge and should try to recover
the losses as soon as possible.
8. Knowledgeable experts can help at the time of financial difficulty and challenging
circumstances by providing their valuable advice. Their advice can help a company to
overcome the losses and survive by taking necessary steps.
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E.I Altman’s Z score
Conclusion
Banking sector was not going well before the emergence of COVID-19. The sector had lower
liquidity ratios and poor financial health. Increase of non-performing loans, capital flight from
Bangladesh, aggressive lending practices are responsible for current liquidity condition in the
banking sector and profitability and financial health are also affected by this. Liquidity is
important as it can affect the most important goal of a business which is profitability. At the
second quarter of 2020, all the liquidity ratios and financial health of the listed banks are
affected badly and the situation has become worse than before. Emergence of this pandemic
affects not only the banking sector; every sector has to face the challenges that are made for
this pandemic. The listed banks had lower cash ratio, current ratio, operating cash flow ratio,
opening cash and cash equivalents, closing cash and cash equivalents prior to the emergence
of this pandemic. These ratios become lower than before in the second quarter of 2020. Only
debt to assets and debt to equity ratios are not changed much at this quarter. All the listed banks
belonged to the red zone in the first quarter and second quarter of 2019. They have the lower
Z-Score in the first and second quarter of 2020. But at the second quarter of 2020, the financial
health of the listed banks becomes poorer than before and the possibility of becoming insolvent
is increased. It is clearly seen that the emergence of this pandemic makes the liquidity condition
and financial health of the listed banks worsen than before. Among the listed Islamic Banks,
First Security Islami Bank Ltd., ICB Islamic Bank Ltd. and Social Islami Bank Ltd. have poor
liquidity ratio than the other listed Islamic Banks. AB Bank Ltd., EXIM Bank Ltd., IFIC Bank
Ltd., Mercantile Bank Ltd., Mutual Trust Bank Ltd. and National Bank Ltd. have the poor
liquidity ratio than the other listed Commercial Banks. Premier Bank Ltd., Rupali Bank Ltd.,
Standard Bank Ltd., Trust Bank Ltd. have lower Z-Score compared to the other listed
Commercial Banks. All the listed Islamic Banks have lower Z-Score and ICB Islamic Bank
Ltd. has negative Z-Score. Though all the listed banks in DSE belong to the red zone, listed
Commercial Banks have generally higher Z-Score than the listed Islamic Banks. It can be said
that there is a negative impact of COVID-19 on the liquidity crisis and financial health of the
listed banks in Bangladesh. This paper can contribute the interesting field of economy and
business.
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E.I Altman’s Z score
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27
Appendix
Z-SCORE VALUE FOR SAMPLE BANKS FROM 2009-2020
2016
2017
2018
2019
2020
0.412725
0.474967
0.239602
0.250027 0.345676 2
0.138806
0.153926
0.250063
0.238406 0.196074 4
0.201047
0.141544
0.156842
0.136547 0.161791 5
0.85941
0.871252
0.938054
0.856598 0.855991 1
-0.75329
-0.67814
-0.61847
-0.60054
-0.61793
Social Islami 0.283284
0.250411
0.265006
0.262417
0.247121 0.261648 3
Dhaka Bank 0.351059
average
Rank
Limited
Prime Bank 0.19917
Limited
Pubali Bank 0.172972
Limited
Uttara Bank 0.754639
Limited
Shahjalal
-0.65367
6
Islami Bank
Limited and
Bank
Limited
Liquidity position
2016
2017
2018
2019
2020
Avera
Rank
ge
Dhaka Bank Limited
0.1772
0.2354
0.2968
0.1020
0.1100
0.1843
16
26
33
51
85
22
0.0599
0.0024
0.0105
0.0844
0.0929
0.0500
88
37
34
44
32
67
0.0031
0.0019
0.0013
0.0075
0.0079
0.0043
72
19
24
62
16
79
0.5068
0.5828
0.5963
0.6382
0.5935
0.5835
58
98
46
06
94
81
Shahjalal Islami Bank -
-
-
-
-
-
Limited and
0.7239
0.6666
0.6246
0.5957
0.6194
0.6460
6
6
3
2
1
8
Prime Bank Limited
Pubali Bank Limited
Uttara Bank Limited
27
2
4
5
1
6
E.I Altman’s Z score
Social
Islami
Bank 0.0842
0.0897
0.0939
0.1041
0.1055
0.0955
56
26
83
63
24
3
2016
2017
2018
2019
2020
Averag
Ran
e
k
5
Limited
3
Profitability
Dhaka Bank Limited
0.0135
0.0123
0.0107
0.0108
0.0107
0.0116
91
74
44
79
74
72
0.0086
0.0064
0.0133
0.0143
0.0115
0.0108
39
27
21
61
05
51
0.0220
0.0185
0.0173
0.0233
0.0183
0.0199
3
59
58
65
84
39
0.0124
0.0141
0.0156
0.0192
0.0167
0.0156
55
45
89
39
97
65
Shahjalal Islami Bank 0.0099
0.0129
0.0143
0.0124
0.0139
0.0127
Limited and
91
78
03
32
41
Social Islami Bank 0.0184
0.0127
0.0125
0.0091
0.0079
0.0121
Limited
95
19
28
79
78
Prime Bank Limited
Pubali Bank Limited
Uttara Bank Limited
98
68
28
6
1
2
3
4
28