The Accuracy of Financial Distress Prediction During the COVID-19
Pandemic on Healthcare Sub-Sector Companies
IMMAS NURHAYATI1, ENDRI ENDRI2, TITING SUHARTI1, IMAM SUNDARTA1,
RACHMATULLAILY TINAKARTIKA RINDA1
1Faculty of Economic and Business, Universitas Ibn Khaldun Bogor
Jl. KH Sholeh Iskandar KM 2 Bogor, 16162
INDONESIA
2Faculty of Economics and Business
Universitas Mercu Buana, Jl. Meruya Selatan No. 1, Kembangan, Jakarta Selatan 11650
INDONESIA
Abstract: - During the recent COVID-19 pandemic, most countries are in a phase of slowing economic growth
that causes long-term financial distress and leads to bankruptcy. This paper describes the accuracy of financial
distress prediction of the healthcare sub-sector companies using the Altman Modified Z-Score, Springate, and
Zmijewski methods. The level of accuracy is determined based on the suitability of the calculation results of the
three models with the company’s bankruptcy data published on the Indonesia Stock Exchange and strengthened
by the analysis based on the calculation of the type error I and II. Based on the level of accuracy and error
types I and II, the Springate is the most accurate method in analyzing the financial distress prediction of the
healthcare sub-sector companies with an accuracy rate of 91.4275. Comparing financial performance before
and after the COVID-19 pandemic, the mean difference test shows that there is no significant difference in
financial performance before and after the COVID-19 pandemic.
Key-Words: - bankruptcy, COVID-19 pandemic, economic crisis, financial distress
Received: October 4, 2021. Revised: July 21, 2022. Accepted: August 12, 2022. Published: September 6, 2022.
1 Introduction production caused by low public demand due to the
weakening of people's purchasing power. The
decrease in people's purchasing power causes firm
financial difficulties due to the decline in production
levels [4].
The decrease in the fundamental value of the
firm will be reflected in the weakening of stock
price movements in the capital market. [4]. Capital
markets provide an early indication of the potential
impact of economic shocks. Although signals from
capital market prices are not always accurate,
economic shocks hitting the economy can be seen in
the capital markets before they manifest in the data,
such as GDP or unemployment rates [5]. The
decline in the composite stock price index
experienced a very sharp decline to almost 20% in
June 2020 due to investors' sentiment toward
withdrawing their funds from the capital market [6]
and also because of an uncertainty economic that is
full of risk and unclear when this pandemic will end
[7]. The COVID-19 pandemic has changed the
perspective of investors in making decisions [8].
They have responded to the volatility of the capital
market and the economic conjuncture by reducing
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The world's concern over the systemic impact of the
coronavirus disease (COVID-19) pandemic is a reality.
Since its presence on December 1, 2019, in Wuhan City,
Hubei Province, China has changed the life system [1].
Even the virus that attacks the respiratory system, which
has entered Indonesia since March 2, 2020, has
devastated all aspects of human life. The COVID-19
pandemic has hampered the development to target
economic growth and efforts to resolve socio-economic
problems of the community [2]. One of the most
prominent economic impacts of the COVID-19
pandemic is on the occurrence of recession on
economies. Based on the Central Statistics Agency
(BPS), the Government of Indonesia officially
announced that the COVID-19 pandemic resulted in a
decline in Indonesia's economic growth in 2020 [3].
Based on the Central Statistics Agency (BPS), the
Government of Indonesia officially announced that the
COVID-19 pandemic resulted in a decline in
Indonesia's economic growth in 2020 as a result of
limited economic activity and the decrease in company
equity and leverage which has an impact on
decreasing market capitalization. The declining
performance of the capital market is a response to
the decline in economic growth and the various
consequences that accompany it [4].
Besides the impact on the economic crisis, the
COVID-19 pandemic has also greatly affected
public health problems [9]. Compared with other
industries, the margins of the healthcare industry are
generally very low. Even before the COVID-19
pandemic, several hospitals were operating at
negative margins. After the COVID pandemic
emerged, hospitals had to stop all but very urgent
non-covid treatments. This causes a decrease in
revenue while costs remain high It is interesting to
analyze the financial performance of the healthcare
companies sector during the COVID-19 pandemic
through the calculation of financial distress [10].
Financial distress is related to the company's ability
to meet all obligations such as paying salaries,
obligations to creditors, and others [12].
This study aims to measure the best model to
predict the financial distress of healthcare sub-sector
companies listed on the Indonesia Stock Exchange
using the Modified Altman Z Score, Springate, and
Zmijewski. The best estimation model is a model
that can predict with a high level of accuracy and
low type errors I and II by comparing the results of
model calculations with company calculation data
published on the Indonesian Stock Exchange. This
study also compares differences in financial
performance before and after the COVID-19
pandemic. The ability to predict financial distress
aims to anticipate the consequences if it occurs [13].
The main focus of this research is on the analysis of
financial distress in the healthcare sub-sector
company during the COVID-19 pandemic in
emerging markets, such as Indonesia and it will be
the novelty and originality of this paper.
2 Literature Review
Financial distress is a bad financial condition and
risk of going bankrupt, caused by the amount of
debt [14]. The company's level of financial
difficulty is determined by the ownership of liquid
assets and access to credit facilities to save from
these conditions [15]. Companies with a lower level
of liquidity than total assets have a greater chance of
bankruptcy[16]. The higher (lower) the level of
liquidity, the lower (higher) the financial distress
[17]. Liquidity is defined by the company's ability to
meet its short-term and long-term obligations [18].
Financial distress is classified into four
categories namely economic failure, business
failure, insolvency, and legal bankruptcy [16].
Economic failure due to the instability of the
company's financial condition. Most economic
failures occur quickly and are unpredictable which
leads to an economic crisis for the company [16].
Business Failure shows the company's inability to
run its operations caused by the inability to generate
profits [19]. Insolvency relates to the company's
inability to meet all of its short-term and long-term
obligations by the due date [20]. Insolvency consists
of technical insolvency and insolvency in
bankruptcy. Technical insolvency is related to the
inability to fulfill its debt obligations or obligations
at a predetermined time. Technical insolvency
occurs when a company‘s assets are higher than its
total debt [21]. Technical insolvency is the
forerunner of the company's economic failure
initiated by illiquid conditions [22]. Insolvency in
bankruptcy is a company's financial condition that is
very severe, experiencing a financial crisis beyond
technical insolvency. The debt owned exceeds the
assets owned [23] or the market value is lower than
the book value of the debt [24]. Legal bankruptcy is
a legal statement regarding the bankruptcy of an
institution that has the authority to state whether the
company is truly in a state of bankruptcy or not.
Endogenous and exogenous are factors that cause
business financial difficulties [16]. Endogenous
factors include high expenses working capital
management, inappropriate financial and marketing
control, inaccuracy in selecting projects, production
exceeding financing capacity, and improper
company policy. The exogenous factors include
market demand for a firm's products, local, national,
and international competition, price supply
commodity, macroeconomic factors and
government key sector regulations, and technology
change.
The potential internal factor may be causes of
financial distress divided into financial and non-
financial factors [25]. Financial factors include the
problem of capital structure, the inappropriate debt
to asset ratio, long-term accounts receivable
payment firm's policy, undercapitalization of a
business, and incorrect price calculation. Non-
financial factors include business strategy and
mismanagement in taking risks and firm decisions,
low product promotion and competitiveness, low
employee productivity, and late mitigating problems
[26]. Platt and Platt [27] several problems that cause
financial distress include mismanagement, low
level of equity, inappropriate good business plan,
goals, and marketing strategy, excessive optimism,
and company's weaknesses.
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Žiković [28] distinguishes failure, insolvency,
or "bankruptcy. Failure is the inability to generate a
return on investment, insolvency is the inability of a
company to pay its maturing obligations, while
bankruptcy is a legal meaning. The two most
common factors in bankruptcy are the company
having more debt than assets and the inability to pay
the debt [29]. A decrease in sales revenue due to
reduced consumer confidence in the quality of the
company's products [30] and the decrease in sales
revenue due to reduced consumer confidence in the
quality of the company's products [31]. A decline in
sales, a decrease in profit, and suffer a loss. Losses
that occur continuously can reduce liquidity and
increase leverage. Leverage shows the company's
debts and represents a risk to the firm [32]. High
financial leverage tends to be illiquid, insolvable,
and bankrupt. The higher leverage, the higher risk,
and the higher the cost of capital [23]. Accurately
predictable bankruptcy can help companies to take
action to minimize the risk of business losses and
prevent bankruptcy [29].
The ratios used in predicting financial distress
can be classified into two categories, namely, the
ratio related to the company's ability to generate
profits (profitability ratios) and the company's
ability to meet short, medium, and long-term
obligations (liquidity and solvency ratios) [33, 14,
34, 35]. In general, using the financial distress
model to predict the company's financial condition,
find out the factors that cause financial problems,
and make problems solving. Based on empirical
studies, size, risk of uncertainty, and company debt
are the factors that cause the failure of the
company's business [36].
Various studies have been conducted over the
past three decades to predict corporate financial
distress. The first study to predict corporate
financial distress was conducted by Beaver [37].
Some years later, Altman (1968) developed a new
financial distress model called Altman Z-Score
including profitability ratio, activity ratio, liquidity
ratio, solvency ratio, and leverage ratio. Altman Z-
Score formula has several weaknesses and only can
be applied in go public manufacturing companies
and been updated (1983) for both manufacturing
and non-manufacturing companies and private and
public firms [38].
The Springate financial distress method [40]
consists of 4 ratios to determine whether the
company is in financial distress. Zmijewski [41]
used to predict the possibility of a company's
financial distress using accounting variables to
measure the proportion of financial distress based
on the probit regression model and random
exogenous sampling. The accounting ratios are net
income to total assets, total debts to total assets, and
current assets to total liabilities. The research results
of Kliestik et al. [42] conclude that the models often
used in measuring financial distress in several
groups of countries studied are the current ratio,
total liabilities to total assets ratio, and total sales to
total assets ratio. The empirical result of Zizi et
al.[43] highlight that interest in sales and return on
assets was a significant role in financial distress
prediction. Grover, Altman, Springate, and
Zmijewski's models better predict financial crises on
the Tehran Stock Exchange [44, 45]. On the
Indonesia Stock Exchange, the Zmijewski X-Score
is the most accurate model for predicting financial
distress [46]. The existence of a gap in the results of
previous research regarding which model is the
most accurate in predicting financial distress is one
of the considerations for conducting this research,
especially in the healthcare sub-sector during the
COVID-19 period. Based on the literature review,
the hypotheses that can be built are as follows:
Ha1: Springate is the highest accuracy model rate
compared with Altman and Zmijewski's model in
predicting bankruptcy of healthcare sub-sector
companies delisting on the Indonesia Stock
Exchange (IDX).
Ha2: There is a significant difference between the
company's financial performance before and after
the COVID-19 pandemic.
3 Research Methods
The data used in this study is quantitative data
sourced from the company's financial statements
published on the Indonesian Stock Exchange (IDX).
The company's financial statements are the data
source due to the coverage information of this
report. In addition to describing the company's
financial performance and as a basis for decision-
making for investors, financial statements are also
often used to predict future finances. The data of the
selected companies are accessible on the IDX
website at http://www.idx.co.id and from
http://www.finance.yahoo.com. The data used is
quarterly data starting from the 4th quarter of 2018,
to the 1st quarter of 2021.
Healthcare companies play a role in dealing
with exposure to the COVID-19 virus to support
community healthcare needs, especially in
Pandemic COVID-19. The collected data will be
processed using modified Altman Z-Score,
Springate, and Zmijewski to determine the presence
of financial distress.
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The Altman Z-Score Method
The first Altman formula called the Z-Score
(bankruptcy index) is as follows [38] :
Z = 1,2X₁ + 1,4 X₂ + 3,3X₃ + 0,6X₄ + 1,0X₅
The Altman Z-Score method consists of net working
capital to total assets (X1), retained earnings to total
assets (X2), earnings before interest and taxes to
total assets (X3), a book value of equity to book
value of debt (X4), and sales to the total asset (X5).
The weakness of the first Altman is only be applied
to go public manufacturing companies. The
following revised Z-score method of bankruptcy
prediction can be applied no to public and private
manufacturing companies. The formula used is as
follows. The formula used is as follows [38]:
Z'=0.717X₁+0.847X₂+3.108X₃+0.42
X₄+0.988X₅
The next Altman Z Score modification model is
formulated by eliminating sales to the total asset
(X5). To calculate financial distress prediction, the
modified Altman Z-Score method defined the
distress function in the following formula [47]:
Z"=6.56X₁+3.26X₂+ 6.72X₃ +1.05X₄
X1, X2, X3, X4, and X5 are calculated by the
formula as shown in table 1. The classification of
financial distress, grey area, and non-financial
companies is based on the value of Modified
Altman Z-Score, which is: Z-Score < 1.1, the
company is financial distress category. If Z-Score
1.1 < Z < 2.6, the company is in the gray area
category. If Z-score > 2.6, the company is in non-
financial distress. The Altman Z Score cut-off value
is shown in table 2.
The Springate Method.
The Springate method consists of working capital to
total assets (A), earnings before interest and taxes
to total assets (B), net income before tax to current
liabilities (C), and sales to total assets (D). The
Springate method defined financial distress
estimates with the following formula [40]:
S = 1.03A + 3.07B + 0.66C +0.4D
A, B, and C are calculated by the formula as shown
in table 1.
The Springate cut-off value is as shown in table 2:
Peter and Yoseph [48]. The cut-off value of
Springate is shown in table 2. If S scores < 0.862,
the company may experience financial distress. If an
S score of 0.862 < S < 1.062, it means that the
company is probably in a gray area. If the S score is
while if the S score is higher than 0.861 then the
company is Non-financial distressed. The Springate
cut-off value is as shown in table 2 [40]
The Zmijewski Method.
The Zmijewski method consists of earning after
tax to total assets (X1), total debt to total assets (X1),
and current assets to current liabilities (X3). X1, X2,
X3, and X4 are calculated by the formula as shown
in table 1.
Table 1. Explanatory Variable and Financial Ratio
Method
Financial
Ratio
Notation
Formula
Modified
Altman Z-
Score
Liquidity
X1
Working Capital
Total Asset
Profitability
X2
Retained Earning
Total Asset
Profitability
X3
Profit Before Interest and Tax
Total Asset
Solvency
X4
Book value of Equity
Book Value of Total Debt
Springate
Liquidity
A
Working Capital
Total Asset
Profitability
B
Earning Before Interest and Tax
Total Asset
Liquidity
C
Net Profit before Tax
Current Liabilities
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Profitability
D
Sales
Total asset
Zmijewski
Profitability
X1
Earning After-Tax
Total Asset
Solvency
X2
Total Debt
Total Asset
Liquidity
X3
Current Asset
Current Liabilities
The Zmijewski cut-off value is shown in table 2
[48]. The Zmijewski cut-off value is shown in table
2. If the X score is > 0, indicate that the company is
in financial distress. If the X score is < 0, means the
company is in non-financial distress. The research
results conclude that the models often used in
measuring financial distress in several groups of
countries studied are the current ratio, total
liabilities to total assets ratio, and the total-sales-to-
total-assets ratio. The empirical results of Zizi et al.
[43] conclude the significant financial ratio to
predict financial distress are interest in sales and
return on assets.
Table 2. The Financial Distress Cut-off Value
The Accuracy Level in the Altman Z-Score,
Springate, and Zmijewski Methods
To test the accuracy of the model, the steps taken
are to measure the accuracy rate by comparing the
correct prediction with the number of samples and
calculate the type 1 error and type 2 error. The
accuracy test of the model was used to answer the
hypothesis (Ha1). The level of accuracy is
calculated by the following formula:
Accuracy Rate = Number of Correct Prediction
Number of Sample x 100%
The level of error is the error description on every
model. Type I error is an error that occurs if the
model predicts the sample does not experience
financial distress, in fact, according to the
Indonesia Stock Exchange data, the company is in
financial distress. Type II error is an error that
occurs if the model predicts the sample experienced
financial distress, but according to the Indonesia
Stock Exchange data, the sample is not included in
the financial distress. Type I error and type II error
is calculated as follows:
Type I Error = The number of Types I error
number of sample x 100%
Type II Error = The number of Type II error
number of sample s 100%
The Theoretical Framework of Financial Distress
Method as shown in figure 1
Financial Distress Method
Cut off
Condition
Modified Altman Z-Score
Z” < 1,1
Financial distress
1,1 < Z”< 2,6
Gray area
Z” > 2,6
Non-financial distress
Springate
S < 0,861
Financial Distress
S > 0,861
Non-Financial Distress
Zmijewski
Z > 0
Financial distress
Z < 0
Non-financial distress
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Fig. 1: Theoritical Framework of Financial Distress Method
4 Results and Discussion
The performance metrics of the financial distress
prediction models at eight healthcare
subsector companies in 2018-2021 using the
modified Altman Z-Score, Springate, and
Zmijewski methods are summarized in Table 3
Table 3. Firm Financial Distress Category According to Z-Score, Springate, and Zmijewski
Ticker
Code
Year
Quarter
Z-Score
Springate
Zmijewski
MIKA
2018
IV
10.8690***
2.34458***
-4.071584***
2019
I
9.94267***
4.02643***
-3.525729***
II
9.94301***
2.61349***
-3.772433***
III
9.60583***
2.18424***
-3.872325***
IV
9.70832***
2.18444***
-4.02033***
2020
I
9.85914***
4.10011***
-3.55002***
II
9.24075***
2.69084***
-3.348282***
III
9.70618***
2.29562***
-3.824573***
IV
10.1292***
2.28472***
-4.074812***
2021
I
10.33911***
3.59516***
-3.622004***
SAME
2018
IV
9.75835***
1.20877***
-1.231973***
2019
I
8.89964***
1.79788***
-1.100677***
II
9.91276***
1.34127***
-1.144458***
III
-2.84919*
-1.05371*
-0.414575***
IV
-5.85712*
-0.17274*
-0.304621***
2020
I
-6.81728*
-1.1606*
-0.45637***
II
-9.18992*
-0.77418*
-0.381743***
III
0.95008*
-0.38558*
1.4835539*
IV
3.00151***
-0.24226*
1.5332515*
2021
I
-3.53945*
0.83763*
-1.473819***
SILO
2018
IV
11.0827***
1.38984***
-3.122585***
2019
I
9.93260***
1.16313***
-2.992226***
II
10.3950***
1.05631***
-3.026270***
III
9.74340***
1.23748***
-2.984158***
IV
18.7396***
1.6046***
-2.593768***
2020
I
15.9144***
0.6853*
-2.098012***
II
137.414***
1.86803***
-2.058522***
III
38.4015***
1.05336***
-2.124109***
IV
20.8760***
1.49752***
-2.457180***
2021
I
12.9032***
0.96425***
-2.339203***
HEAL
2018
IV
5.27145***
2.06669***
-1.617150***
2019
I
6.63647***
0.87145***
-1.573409***
II
9.90268***
1.18344***
-1.554341***
III
8.74882***
1.43099***
-1.573340***
IV
9.81471***
1.70523***
-1.582245***
2020
I
8.25423***
1.27195***
-1.393765***
Z-Score Modification Method
Z= 6,56X1 + 3,26X2 + 6,72X3 +
1,05X4
Financial
Analysis
Springate Method
S=1,03A + 3,07B + 0,66C +
0,4D
Zmijewski Method
Z= ‐4,3 – 4,5X1 + 5,7X2 +
0,004X3
Accuracy Levels
Type I Error
Type II Error
Financial Report of
Delisting
Companies from
Indonesia Stock
Exchange (IDX)
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II
7.07593***
1.07221***
-1.571599***
III
10.3412***
1.44571***
-1.463042***
IV
7.19202***
1.7664***
-1.628895***
2021
I
7.00563***
1.28444***
-1.461654***
PRIM
2018
IV
1.85306**
0.53678*
-3.924140***
2019
I
2.39863**
0.70472*
-3.888701***
II
2.31002**
0.44371*
-3.891543***
III
2.30945**
0.42371*
-3.781726***
IV
3.33572***
0.52554*
-3.872552***
2020
I
4.00882***
1.2466***
-3.909879***
II
4.46936***
0.87436***
-3.994306***
III
5.69221***
0.82765*
-4.089326***
IV
7.52189***
1.03787***
-4.061103***
2021
I
7.94346***
2.22089***
-3.796787***
PRDA
2018
IV
27.7929***
2.95714***
-3.460261***
2019
I
24.3739***
3.85728***
-3.225030***
II
28.9852***
2.7682***
-3.271596***
III
27.0208***
2.7641***
-3.353484***
IV
23.4460***
3.05648***
-3.634795***
2020
I
21.7862***
4.11035***
-3.088542***
II
26.2007***
2.78981***
-2.958513***
III
21.3457***
2.60464***
-3.295978***
IV
19.132***
2.86557***
-3.535678***
2021
I
17.8466***
3.35173***
-3.376197***
SRAJ
2018
IV
1.47631**
-0.69437*
-1.953515***
2019
I
1.83210**
-2.0714*
-1.919421***
II
2.11245**
-1.23981*
-1.875704***
III
2.71292**
-0.78718*
-1.657645***
IV
3.55949***
-0.98338*
-1.320620***
2020
I
5.66359***
-5.17701*
-1.000758***
II
3.19113***
-2.16035*
-0.275503***
III
-10.6166*
3.45442***
-5.542824***
IV
7.16210***
-1.17566*
-0.292128***
2021
I
7.23084***
-2.54985*
-0.488890***
*Financial Distress, **Grey Area, ***Non-Financial Distress
Based on the Altman Z-Score modification
method analysis, SAME in the third and fourth
quarters of 2019, the first, second, and third
quarters of 2020, and the first quarter of 2021 are in
financial distress. PRIM and SRAJ in the fourth
quarter of 2018 and the first - third quarter of 2019
are in the gray area. SRAJ is in financial distress in
the third quarter of 2021. While in other periods, all
companies are in non-financial distress.
The analysis of financial distress predictions
using Springate found that SAME in the third-
fourth quarter of 2019, the first - fourth quarter of
2020, and the first quarter of 2021 are in financial
distress. SILO in the first quarter of 2020 is in
financial distress. PRIM and SRAJ in the fourth
quarter of 2018, the first - fourth quarter of 2019,
SRAJ in the first, second, and fourth quarter of
2020, and the first quarter of 2021 are in financial
distress. PRIM in the third of 2020 is in financial
distress. All companies in other periods are in non-
financial distress.
The analysis of financial distress predictions
using Zmijewski found that only SAME in the third
and fourth quarter of 2019 are experiencing
financial distress. All companies in other periods
are in non-financial distress.
Level of Accuracy and Error Rate
The accuracy level of financial distress
prediction was based on the correct number of
predictions divided by the total data and multiplied
by 100%. The correctness predictions are measured
by comparing the output of the model in the study
period (t) with the debt to equity ratio (DER) in the
following year (t + 1).
The debt to Equity Ratio (DER) is the ratio used
to assess debt to equity [49]. This ratio is found by
comparing all debt, including current debt, and
total equity. Debt to equity ratio (DER) is a
financial ratio to measure the level of solvency of a
company's ability to meet all of its obligations.
There is a significant relationship between the
solvency ratio and the level of financial difficulty,
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indicating that a cash flow ratio is reliable for
predicting financial distress [14, 50]. DER is a
reference for categorizing a company in financial
distress or not. The company is in financial distress
if it is not balanced with the availability of
sufficient funds to pay off its debts. The higher the
financial risk, the higher the company is in
financial distress [51]. A higher ratio, especially
above 1.0, indicates that a company is significantly
funded by debt and may have difficulty meetings
its obligations. Generally, DER below 1.0 is
relatively safe, whereas ratios of 2.0 or higher
would be considered risky.
Table 4 shows the level of accuracy model
using the Altman Z-Score Modification, the
Springate, and the Zmijewski method. The
modification Altman Z-Score method produces the
lowest accuracy rate which is 28.57% in the fourth
quarter of 2020, the Zmijewski method produces
the lowest accuracy rate which is 28.57% in the
second quarter of 2020. The Springate method
produces the lowest accuracy rate which is 57.14%
in the second quarter of 2020.
Springate is a financial distress prediction
model with the highest average accuracy of
91.427% with average type I error and the lowest
type II error of 17.14%. Zmijewski is a financial
distress prediction model with the lowest average
accuracy of 65.714% with average type I error and
the highest type II error of 34.29% and 40.00%,
respectively. The level of accuracy of the Altman
Z-Score modification method of 68.571% is better
than Zmijewski in 65.714%. The average type I
error and type II error of Zmijewski is about
34.29% and 40.00%.
Table 5 shows a different test of the average
level of accuracy of the financial distress model.
The accuracy of Springate in bankruptcy prediction
is significantly different from Modified Altman's
Z-score and Zmijewski at the 0.05 significance
level. While although the Modified Altman Z-
Score model performs better accuracy than
Zmijewski, the statistical results in predicting
bankrupt firms are insignificant. Thus, it cannot say
one model is superior to other models for
companies in the health care sub-sector during the
COVID-19 pandemic.
The results of this study are not in line with
the results of previous studies conducted by Salim
and Ismudjoko [52] that concluded the Modified
Altman is the most accurate predictive model with
the highest accuracy rate. Springate is the lowest
accuracy rate among the coal mining sector firms
listed on Indonesia Stock Exchange (IDX) for 2015
– 2019.
Table 4. The Accuracy Level of Financial Distress (FD) and Non-Financial Distress (NFD)
Prediction and Error Rate
Method
Year
Quarter
Prediction
Real
Level of
Accuracy (%)
Type I
Error
(%)
Type II
Error
(%)
FD
NFD
FD
NFD
Altman
Modifikasi
Z-Score
2018
IV
0
7
2
5
71.43
28.57
28.57
2019
I
0
7
2
5
71.43
28.57
28.57
II
0
7
2
5
71.43
28.57
28.57
III
1
6
3
4
71.43
28.57
28.57
IV
1
6
4
3
57.14
28.57
42.86
2020
I
1
6
2
5
85.71
14.28
14.28
II
1
6
5
2
42.86
57.14
14.28
III
2
5
3
4
85.71
14.28
14.28
IV
7
0
2
5
28.57
71.43
71.43
2021
I
1
6
1
6
100
0
0
Average
68.571
17.14
17.24
Springate
2018
IV
2
5
2
5
100
0
0
2019
I
2
5
2
5
100
0
0
II
2
5
2
5
100
0
0
III
3
4
3
4
100
0
0
IV
3
4
4
3
85.71
14.28
14.28
2020
I
3
4
2
5
85.71
14.28
14.28
II
2
5
5
2
57.14
42.86
42.86
III
3
4
3
4
100
0
0
IV
2
5
2
5
100
0
0
2021
I
2
5
1
6
85.71
14.28
14.28
Average
91.427
17.14
17.14
Zmijewski
2018
IV
0
7
2
5
71.43
28.57
28.57
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2019
I
0
7
2
5
71.43
28.57
28.57
II
0
7
2
5
71.43
28.57
28.57
III
0
7
3
4
57.14
42.86
42.84
IV
0
7
4
3
42.86
57.14
57.14
2020
I
0
7
2
5
71.43
28.57
28.57
II
0
7
5
2
28.57
71.43
71.43
III
1
6
3
4
71.43
28.57
57.14
IV
1
6
2
5
85.71
14.28
42.84
2021
I
0
7
1
6
85.71
14.28
14.28
Average
65.714
34.29
40.00
Several factors that cause the accuracy
differences are the differences in the sample or the
comparison tools used, including the auditor's
opinion and the company's income in the next
period. With so many considerations in
determining the level of model accuracy, it is
difficult to say or claim that one model is more
accurate than another. The results of this study can
add insight and knowledge on how to predict
financial distress in the period leading up to and
during the COVID-19 pandemic in the healthcare
sub-sector published on the Indonesia Stock
Exchange, that Springate is a bankruptcy analysis
tool that is better than other analytical tools.
Table 5. Average Difference Test Accuracy Level of Financial Distress Measurement
Modified
Altman Z-Score
Springate
Modified Altman
Z-Score n
Zmijewski
Springate
Zmijewski
Mean
68.571
91.427
68.571
65.714
91.427
65.714
Variance
444.4191
190.5143
444.4191
326.50432
190.5143
326.5043
t Stat
-2.868377
0.32539
3.576018
P(T<=t)
0.00511
0.374319
0.00108
t Critical
1.734064
1.734064
1.734064
To find out the financial distress difference
before and after the pandemic, the difference test is
carried out as presented in table 6. The average
difference test shows that the financial distress
difference after and before COVID-19 is
insignificant, indicated by the p-value of 0.89
>alpha (5%) and t stat < t critical.
Table 6. The Average Difference Test of Financial
Distress Before and After Pandemic COVID-19.
Before COVID-19
After COVID-19
Mean
1.212723714
1.156332857
Variance
2.109884699
3.899942031
t Stat
0.136085465
P-Value
0.892155792
t Critical
1.995468931
5 Conclusion
The lack of research on predicting financial distress
in the period before and during COVID-19 at
healthcare subsector companies in Indonesia has
encouraged this research. The purpose of this study
is to determine the most relevant predictor to
identify the occurrence of financial distress and to
find out the financial distress difference before and
after the pandemic COVID-19.
To achieve this objective, three predictor
models of financial distress were used. Empirical
results on the test sample using the Altman Z-
Score, Springate, and Zmijewski concluded the
Springate method is the most accurate and
appropriate method for measuring financial distress
in healthcare sub-sector companies at the 0.05
significance level. While although the Modified
Altman Z-Score model performs better accuracy
than Zmijewski, the statistical results in predicting
bankrupt firms are insignificant. Thus, it cannot say
one model is superior to other models for
companies in the health care sub-sector during the
COVID-19 pandemic.
Based on the average difference test, there are
no significant financial condition differences in the
healthcare sub-sector before and after the COVID-
19 pandemic. Although there is no significant
difference between before and after the COVID-19
pandemic, the average score on the Springate
shows a decreased value after COVID-19.
The results have practical implications for
creditors and managers in making financing
decisions to pay attention to the company's
financial adequacy to avoid losses. In the interest of
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academics, this research provides the empirical
result that Springate is significantly more accurate
than the other models used.
Constrained by the weaknesses in this study,
suggestions for further research can be developed
by increasing the number of research samples,
adding qualitative variables and other test models,
and considering macroeconomic factors that affect
the company's financial condition. Finally, relevant
research in the future can use this research as a
reference by comparing this research results with
other relevant research.
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.132
Immas Nurhayati, Endri Endri,
Titing Suharti, Imam Sundarta,
Rachmatullaily Tinakartika Rinda
E-ISSN: 2224-2899
1474
Volume 19, 2022
Sciences and Business Research, Vol. 8, No.
4, 2019, pp. 11-17.
[52] Salim, M.N, and Ismudjoko, D, An Analysis
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https://doi.org/10.32996/jefas.2021.3.2.1
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Conceptualization: Immas Nurhayati, Titing
Suharti. Data curation: Iman Sundarta, Immas
Nurhayati. Formal analysis: Endri Endri, Immas
Nurhayati. Funding acquisition: Rachmatullaily
Tinakartika, Titing Suharti. Investigation: Titing
Suharti, Endri Endri. Methodology: Endri Endri,
Immas Nurhayati. Project administration: Titing
Suharti, Rachmatullaily Tinakartika. Resources:
Iman Sundarta, Rachmatullaily Tinakartika.
Software: Endri Endri, Immas Nurhayati.
Supervision: Endri Endri, Iman Sundarta.
Validation: Immas Nurhayati, Titing Suharti.
Visualization: Immas Nurhayati, Iman Sundarta.
Writing – original draft: Immas Nurhayati.
Writing – review & editing: Endri Endri
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.e
n_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.132
Immas Nurhayati, Endri Endri,
Titing Suharti, Imam Sundarta,
Rachmatullaily Tinakartika Rinda
E-ISSN: 2224-2899
1475
Volume 19, 2022