JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI 
Vol. 24, No. 4, 2022, pp. 34-52 

Published online  in http://jos.unsoed.ac.id/index.php/jame  
ISSN: 1410-9336/E-ISSN: 2620-8482 

  
Correspondence to : alfiyanwahyu_wicaksono@yahoo.com   
  

Received: 28 Aug 2022 
Accepted: 12 Nov 2022 
Published: 1 Dec 2022 

 

INTRODUCTION  
Financial distress is a condition of a 

company experiencing a bad financial 
condition, namely when the company cannot 
fulfill its obligations to third parties 
(Kamaluddin et al., 2019). Companies 
experiencing financial distress will face 
financial difficulties and worsening financial 
performance (Kazemian et al., 2017). In early 
2020 the Indonesian government officially 
announced that the COVID-19 virus had 
become a pandemic in Indonesia with the 
establishment of the Task Force for the 
Acceleration of Handling Covid-19 on March 
13, 2020 through Presidential Decree No. 07 
of 2020. After the formation of the Covid-19 
task force, the government issued PP No. 21 
of 2020 on large-scale social restrictions. The 
weakening economy due to the global crisis 
triggered by the COVID-19 pandemic has 
affected the company's condition both directly 
and indirectly. The existence of regulations 
limiting physical distance that must be carried 
out makes people reduce activities outside the 
home. Physical activity that is hampered 
makes the economy a bit hampered. 

According to Statistics News No. 83/11/Th. 
XXIV gross domestic growth in Indonesia in 
the first quarter of 2020 decreased to 2.97% 
from 4.96% in the previous quarter. 

Indonesia's economic growth in 2020 was the 
first time it touched negative numbers since 
1998, which was more precisely in the second 
quarter of 2020, which decreased to -5.32%. 
In the next quarter, the Indonesian economy 
experienced an increase but was still at a 
negative level, until in the fourth quarter of 
2020 at -2.19%. The sector that was most 
affected at the end of 2020 was the trade 
sector which experienced a decline at -3.72%, 
followed by the construction sector at -3.26% 
and the sector that did not touch the negative 
level, one of which was the agricultural sector 
at 1.75%.. Indonesia's economic situation 
continued to improve until in the second 
quarter of 2021 for the first time it touched a 
positive level since the pandemic occurred, 
namely at 7.07%, then decreased again to 
3.51% in the third quarter of 2021. According 
to the Indonesia Stock Exchange report, IDX 
yearly 2019 and 2020 growth in the property, 
real eastat, and building construction sectors 
at the end of 2019 was at 2,052, while at the 
end of 2020 there was a drastic decline to 396. 

The trade sector is the sector that 
experienced the highest decline compared to 
other sectors, including the services trade 
sector. One of the companies affected by the 
COVID-19 pandemic is a company engaged in 
hospitality services. Where with the physical 

  

Analysis of the Effect of Financial Ratio on Financial 
Disress in the Hospital Sector in the List of Sharia 
Securities Affected by the Covid-19 Pandemic  
 
Alfiyan Wahyu Wicaksono1, Imam Agus Faisol2 

 
1,2Accounting, Trunojoyo Madura University, Indonesia 

 

Abstract 

 

This study aims to examine the effect of the current ratio, debt to asset ratio, return on assets 
and tobin's q in influencing the incidence of financial distress in hotel sector companies 
affected by the COVID-19 pandemic. The data used in this study is secondary data obtained 
from the company's financial statements in 2020 - 2021. The object of this research is the 
hotel sector company which is listed on the Indonesia Stock Exchange and is included in the 
category of the Sharia Securities List. This study uses a purposive sampling method in 
determining the research sample, then obtained 8 companies with a total of 56 financial 
reports from the first quarter of 2020 to the third quarter of 2021. The hypothesis testing used 
is panel data regression with a significance level of 5%. The results of this study are showing 
that return on assets have no effect on financial distress. Meanwhile, the current ratio, debt to 
asset ratio, and Tobin's q have an effect on financial distress.  

  

Keywords Financial distress; covid-19; hotel sector companies  
 

 

 

  



 
 
JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 4, 2022 pp. 34-52 

 

restrictions and the prohibition of activities 
outside the home, the number of hotel service 
users is decreasing. However, with 
diminishing income, companies still have to 
pay employee salaries, so many companies 
decide to terminate their employment to 
reduce the company's burden. Companies 
have to limit their operating hours and also 
have to apply WFH (Work From Home) to 
some employees. However, even with the 
reduction in expenses, if it is not balanced with 
an increase in income, it will still not get the 
maximum profit, so that the company's cash 
flow is not smooth. The situation of financial 
difficulties can get worse if the company is 
illiquid, namely when costs increase and 
revenues decrease, especially in conditions of 
economic crisis (Kamaluddin et al., 2019). It 
can be concluded that non-current company 
liquidity can increase the chances of financial 
distress. In addition to liquidity, solvency and 
profitability also have a relationship with 
financial distress (Kamaluddin et al., 2019). 

One of the characteristics of a company 
experiencing financial distress is when the 
company has difficulty accessing credit 
(López-Gutiérrez et al., 2015). Can be 
concluded that when the company is in a 
condition of financial difficulty and difficulty in 
accessing credit, it can be interpreted that the 
company is in a condition of financial distress. 

The emergence of financial distress in a 
company which is reflected in the company's 
report can be used as a signal used by 
investors in making decisions in a company. 
Financial distress signals can also be used by 
management and creditors. Management will 
be able to make the right decisions when signs 
of financial distress appear, so that the 
possibility of bankruptcy in a company can be 
overcome before bankruptcy occurs. Financial 
distress can also assist creditors in analyzing 
the company's condition before making a 
decision to grant a loan to the company, 
whether the company can repay the loan 
(Kazemian et al., 2017). 

The results of research by Iswari and 
Nurcahyo (2020) state that the current ratio 
has no influence on the emergence of financial 
difficulties, but research conducted by 
Kazemian et al., (2017) mentions different 
things, the study explains that the current ratio 
can be used in analyzing the emergence of 
financial difficulties. The current ratio can 
show the capability of a company in settling its 
current liabilities, besides that this ratio is the 
ratio used in assessing the liquidity of a 
company. Research conducted by Iswari and 

Nurcahyo (2020) shows that there is evidence 
that return on assets has an impact on the 
emergence of financial difficulties. Return on 
assets indicates the company's capability to 
earn a profit, the ratio of return on assets is 
used as an indicator of a company's 
profitability. 

Kazemian et al., (2017) say that the Tobin's 
q ratio has a significance in the occurrence of 
financial difficulties, in their research it is also 
stated that the lower the Tobin's q value, the 
greater the possibility of financial distress. The 
high value of Tobin's q ratio reflects that a 
company is considered more valuable by the 
market, thus indicating that the company is in 
optimal condition and has the ability to be 
better in the future. 

The research focuses on companies listed 
on the Sharia Securities List. The list of sharia 
securities was chosen because only sharia 
shares whose growth was in line with 
economic conditions in Indonesia. According 
to the Indonesia Stock Exchange report, IDX 
Statistics 3rd Quarter 2021 recorded the 
growth of the Indonesia Sharia Stock Index 
YTD (year to date) as of December 2020, 
which decreased by -5.46%, at which time 
Indonesia's economic growth was at -2.19% 
and in the third quarter three growth in the ytd 
of sharia shares was at a positive number, 
namely 2.29%, at which time Indonesia's 
economic growth was also at a positive level 
of 3.51% and while other stock indices were at 
a negative level, namely the LQ45 index, 
Kompas 100, and Bisnis-27 which the index is 
contrary to Indonesia's economic growth. It 
can be said that sharia shares are 
experiencing growth in line with Indonesia's 
economic growth, so it is necessary to further 
investigate the potential for financial distress 
in sharia shares, especially in the hotel sector. 
The results of this research can later be used 
by management to make decisions in 
anticipating financial distress. In addition, this 
research can also help investors to make 
investment decisions in a company. Based on 
previous research, this study examines in 
depth the current ratio, debt to asset ratio, 
return on assets and Tobin's q in influencing 
the incidence of financial distress as 
measured using Altman z score analysis. 
 

Literature review 

Signaling theory  
The theory used in this research is signal 

theory. Signal theory is used in providing an 
explanation that a financial report can convey 
positive signals and negative signals to the 



 
 
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parties who use financial statements. 
According to Agustini and Wirawati (2019), the 
higher the company's liquidity can convey a 
positive signal to investors and creditors, 
because with high liquidity the company is 
considered to have the capability to cover all 
of the company's current liabilities. 

Signal theory uses the basis that 
information obtained by interested parties is 
likely not the same. Signal theory can be 
related to the presence of asymmetry or 
information inequality. Signal theory shows 
that there is an inequality of information 
received between management and other 
parties, so management is required to convey 
information needed by interested parties to the 
company. 

Signal theory is an explanation that a 
company can convey signals to interested 
parties. The signal in question is information 
about the company's performance and 
management in running the company. Signals 
can be in the form of activities to achievements 
that have been made by the company, one of 
which is a signal in the form of dividend 
distribution. The achievements of the 
company can also be a signal, namely 
information that shows that the company's 
performance has improved compared to the 
previous year. 

The published financial reports will be 
accepted by stakeholders and will be 
interpreted as a good signal or a bad signal. 
One of the signals used is the level of profit 
and loss submitted by the company through 
the issuance of profit/loss reports. The 
profit/loss report can be used as a good signal 
or a bad signal. The company's profit 
submitted in the financial statements by the 
company has increased, so that information 
can be classified as a good signal or a positive 
signal, because an increase in company 
profits indicates that the company's condition 
is in optimal condition and vice versa if the 
company's profit shows a declining condition, 
it can be said that the company is experiencing 
a less than optimal condition so that it can be 
classified as a bad signal. 

Low profitability is a signal of the 
company's limitations in converting revenue 
streams into profits, so it can be interpreted 
that the greater profitability value means that 
the possibility of financial distress can occur is 
getting smaller (Kazemian et al., 2017). 
Meanwhile, according to Lotfi (2018), to form 
a credible signal and not misinterpret, there 
must be alignment of interests with relevant 
stakeholders. It can be concluded that 

financial statements can be used as a signal 
for users of financial statements that are used 
as consideration when making a decision. 

Indonesian sharia stock index 
One of the developments in the Islamic 

economy in Indonesia is the ISSI (Indonesian 
Sharia Stock Index). The Islamic stock index 
is a summary that shows the performance of 
Islamic stocks listed on the Indonesia Stock 
Exchange. The Indonesian sharia stock index 
consists of all companies included in the 
sharia securities list (DES). The list of sharia 
securities is a list of all companies listed on the 
Indonesia Stock Exchange whose business 
activities do not violate the provisions of 
Islamic law. The list of periodic sharia 
securities is issued periodically by the Board 
of Commissioners of the Financial Services 
Authority. 

The list of sharia securities was first 
published by Bapepam-LK through regulation 
no. IX.A13 regarding the issuance of sharia 
securities and No. IX.14 concerning contracts 
used in the issuance of sharia securities, 
which was followed by the issuance of a list of 
sharia securities on September 12, 2007 
(Fauzani, 2021, p. 4). Sharia shares at that 
time only amounted to 174 shares, but until 
now it has experienced significant growth until 
2020 the number of sharia shares amounted 
to 457 shares. At present, the list of sharia 
securities is no longer issued by Bapepam-LK, 
but is issued by the Board of Commissioners 
of the financial services authority. 

As of May 12, 2011, the Indonesian stock 
exchange had two sharia stock indices, 
namely the Jakarta Islamic Index (JII) and the 
Indonesian Sharia Stock Index (ISSI) (Azis et 
al., 2015, p. 71). The difference between the 
sharia index and other indices is that its 
issuance is not only based on the law, but 
must also be based on the MUI fatwa so that 
it can be ensured that the shares included in 
the sharia stock index are in accordance with 
the provisions of Islamic law. Fatwa 
concerning the capital market that has been 
issued, among others, is Fatwa No. 40/DSN-
MUI/X/2003 concerning Capital Markets and 
General Guidelines for the Implementation of 
Sharia Principles in the Capital Markets MoU 
of Bapepam & LK with DSN-MUI, Fatwa No. 
65/DSN-MUI/III/2008 concerning Sharia Pre-
emptive Rights (Preemptive Rights), and 
Fatwa No. 80/DSN-MUI/III/2011 concerning 
the Application of Sharia Principles in the 
Mechanism of Trading Equity Securities in the 
Regular Market of the Stock Exchange. 



 
 
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Issuance of a list of sharia securities, the 
Board of Commissioners of the financial 
services authority must refer to the criteria for 
the issuance of sharia securities. The criteria 
for the issuance of sharia securities are 
regulated in the Financial Services Authority 
Regulation No. 35/POJK.04/2017 concerning 
Criteria and Issuance of Sharia Securities List. 
According to Article 2 paragraph 1 letter b, the 
criteria for sharia shares are as follows, do not 
carry out activities and types of business that 
are contrary to sharia principles, one of which 
is the sale of goods or services that are haram 
not because of the substance (haram li-
ghairihi) stipulated by the National Sharia 
Council - Indonesian Ulema Council and 
goods or services that damage morals and are 
harmful. Apart from the aspect of the object of 
business activity, the company must also meet 
financial requirements, namely total interest-
based debt compared to total assets of not 
more than 45% and total interest income and 
other non-halal income compared to total 
operating income and other income of not 
more than 45%. than 10%. 

Based on the Financial Services Authority 
regulation, it is clear that in order to determine 
the list of sharia securities which will later be 
included in the Indonesian Sharia Stock Index, 
a strict screening process has been carried 
out. The stringent regulations can eliminate 
public doubts about the presence of sharia 
shares and thus increase the number of 
investors in sharia shares in Indonesia. 

Current ratio 
One of the methods used in assessing high 

or low liquidity in a company is to use the 
current ratio or current ratio. The current ratio 
explains how a company's current liabilities 
can be covered by assets that can be 
converted into cash in a short time. The 
current ratio can be used as an indicator of a 
company's liquidity level. 

In the research of Maulida et al., (2018), it 
is explained that the current ratio is the ratio 
used in assessing the capability of a company 
to meet current liabilities using current assets. 
The study also explains that the current ratio 
has a strong ability to measure liquidity. A 
current ratio with a high value can explain that 
a company will be more efficient in managing 
current assets to meet its obligations. 

A high current ratio is an indicator that 
shows the company's capability to settle 
current liabilities using current assets and 
conversely a low current ratio is a sign that the 
company does not have the ability to use 
current assets to meet current liabilities 

(Kazemian et al., 2017). A high current ratio 
value can mean that a company's liquidity is 
also getting higher, this is because the 
company has the ability to pay or is able to 
settle its current liabilities. 

A current ratio that is too high can also be 
considered bad for the company, because it 
can be assumed that a lot of funds will not be 
used productively. A high current ratio can 
also show that the company has excess cash 
compared to the level of need and vice versa, 
a fairly low current ratio means that the 
company is in liquidity problems. 

Debt to asset ratio 
Debt to asset ratio is a ratio that can be 

used to measure solvency, by assessing the 
ratio of total debt and total assets of the 
company. It can be interpreted that the debt to 
asset ratio is the size of a company's assets 
funded by debt (Sumani, 2020). The higher 
value of the debt-to-asset ratio means that 
more of the company's assets are obtained 
through debt. The higher the liability of a 
company, the higher the interest on debt that 
must be paid by the company. 

Debt to asset ratio is a debt ratio used in 
assessing the comparison between total debt 
and total assets. Debt in question is all debt 
that the company includes current liabilities 
and non-current liabilities. Creditors like a debt 
ratio with a low value, this is because with a 
low debt ratio, the possibility of debt 
repayment will be greater so that the risk of 
bad credit is lower. 

In practice, if the company has a large 
solvency ratio, there is a high possibility of 
losses caused by high debt, but there is also 
the possibility of an opportunity to get a large 
profit if the debt can be managed optimally. 
Conversely, if the company has a low solvency 
ratio, it has a low risk of loss, but reduces the 
rate of return because most of the funding is 
only done with equity. Management must 
manage the solvency ratio optimally, with 
optimal management, the company will get 
high returns even though it has high solvency. 
It should be noted that the high or low this ratio 
depends on the liabilities owned by the 
company, in addition to the assets owned by 
the company (equity). 

Return on assets 
Return on assets is a ratio that can be used 

in assessing the capability of a company to 
earn a profit (Daryanto et al., 2019). The return 
on assets value is obtained by comparing net 
income with the total assets owned by the 
company. The increase in the value of the 
return on assets means that the company's 



 
 
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ability to earn profits is getting better, so this is 
good for the company. 

       Return on assets is a ratio that has a 
relationship to profitability, namely assessing 
the capability of a company in obtaining 
profits. Determinants of return on assets are 
net income and total assets. The net profit in 
question is the last number in the profit/loss 
statement. Net income is the difference 
between income and expenses. The assets in 
question are assets owned by the company to 
conduct business. Total assets are all assets 
owned by a company in the form of tangible 
assets and intangible assets. 

Tobin's q ratio 
Tobin's q ratio is the ratio used to provide 

an assessment of a company. Tobin's q is one 
of the ratios used to measure the value of the 
company, which is a measuring tool that 
defines the value of the company in tangible 
and intangible assets (Dura, 2022:56). Tobin's 
q value is obtained from the comparison 
between the market value and book value of 
liabilities with the book value of assets 
(Mediyanti et al., 2021). The higher the Tobin's 
q value, the more valuable the company is, it 
can also be interpreted that the company is in 
optimal condition. 

The advantage of Tobin's q is that this ratio 
can be used in making investment decisions. 
Tobin's Q can show the value of a company so 
that the information can later be relevant to 
investment decisions that will be made. The 
company will increase the share capital if the 
tobin's q ratio is high, this is because if the 
tobin's q value is high, the company will be 
able to get a higher rate of return. The 
company's assessment using Tobin's Q not 
only conveys an assessment through the 
company's fundamentals, but also measures 
the size of a market assessing the company. 

Tobin and Brainard (1976) in their research 
on Tobin's q are as follows: 

“One, the numerator, is the market 
valuations: the going price in the market for 
newly produced commodities. We believe that 
this ratio has considerable macro-economic 
significance and usefulness, as the nexus 
between financial markets and markets for 
goods and services” 

Tobin's q is a calculation that explains the 
value of the company through tangible assets 
and intangible assets. The small value of 
Tobin's q indicates that the cost of replacing 
the company's assets is greater than the 
market value of the company. A low Tobin's q 
value is also a sign that the market values a 
company low. Conversely, if the Tobin's q 

value of a company is high, the company's 
value is greater than the book value of the 
company's assets. 

Financial distress 
Financial distress is a condition of a 

company that is in a condition of unstable 
financial performance that starts from the 
company's cash flow that is not smooth and 
the company's sales decline, resulting in a 
decrease in the company's profit. According to 
Beaver (1966) in Vianez et al., (2019) "stated 
that financial distress is based on the 
theoretical framework of cash flow models, by 
performing a comparison between water 
deposits and companies in financial 
difficulties, as resources that were draining 
away." 

Financial distress is a condition of a 
company that is experiencing financial 
difficulties which is characterized by a 
negative net profit for two years and if no 
improvement efforts are made, bankruptcy 
can occur (Sumani, 2020). From this 
explanation, it can be concluded that financial 
distress can be a signal for the company to 
immediately make improvements to prevent 
bankruptcy. 

Conceptual framework 
The conceptual framework used in the 

study is as follows: 

Figure 1.  Research Conceptual 
Framework 

 
The approach used in detecting financial 

difficulties that occur in a company is through 
financial ratio analysis. Based on Figure 1, this 
study will examine whether the variables 
current ratio, debt to asset ratio, return on 
assets and Tobin's q can affect financial 
distress in a company. 

Variable X1 is the current ratio, which is the 
variable used to determine the level of the 
company's current debt to the company's 
current assets. The debt to asset ratio in the 

Financial 
distress (y)

Current ratio (x1)

Debt to asset ratio (x2)

Return on asset (x3)

Tobin’s q (x4)



 
 
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X2 variable is used in assessing the 
company's debt level to the company's assets. 
In the X3 variable, the return on assets is used 
to see the level of the company's capability to 
earn a profit, then for the X4 variable, namely 
Tobin's q ratio, the ratio used to measure the 
value of the company which is assessed from 
the stock price. The Y variable in this research 
is financial distress, to measure the Y variable, 
the Altman z score is used in assessing the 
size of financial distress in the company. 
 

Hypothesis 
Based on empirical studies, theoretical 

studies, and the conceptual framework of the 
research, the hypotheses in this study are as 
follows: 

Effect of current ratio on financial 
distress 

The current ratio is an indicator that shows 
that the company has the ability to settle the 
company's short-term liabilities with current 
assets and vice versa, a high current ratio is a 
sign that the company has the ability to use 
current assets to meet short-term liabilities 
(Kazemian et al., 2017). 

The current ratio can be used to measure 
the company's liquidity level. The liquidity of a 
company can be used as a signal for users of 
financial statements. According to Agustini 
and Wirawati (2019), high liquidity is able to 
give a positive signal to investors and 
creditors, the company is considered to have 
the ability to meet current liabilities and is able 
to manage the company. Signaling theory can 
be used to explain that the current ratio can 
provide a signal for users of financial 
statements, where the greater the value of the 
current ratio, the better the financial condition 
of a company. However, a liquidity ratio that is 
too high can also indicate that the company's 
working capital is not productive. 

Unproductive working capital can result in 
costs that can reduce the company's profit and 
in the end can lead to the possibility of 
financial distress. The results of research by 
Nurhamidah and Kosasih (2021) explain that 
the current ratio has an influence on financial 
distress. Research conducted by Nurhidayah 
and Rizqiyah (2017) also explains the same 
thing, namely the current ratio has an 
influence on financial distress. Based on this 
explanation, the hypotheses used are: 
H1: Current ratio has an effect on financial 
distress. 
 
 

Effect of debt to asset ratio on financial 
distress 

According to Iswari and Nurcahyo (2020) 
financial theory generally states that the 
amount of debt in a company can cause a 
large interest expense, and can lead to 
financial distress. This condition is in 
accordance with one of the characteristics of 
the emergence of financial distress where the 
company is unable to fulfill its obligations. 
However, the study also explained that with 
the amount of debt, the company can get a 
high level of return from the debt management 
activities (high risk high return). With a high 
rate of return, the company can pay its 
obligations. 

In signaling theory the debt to asset ratio 
can be used as a signal to users of financial 
statements. The smaller debt to asset ratio 
can be a positive signal and vice versa where 
the high debt to asset ratio can be a negative 
signal for users of financial statements. 

The results of research from Sumani 
(2020) show results where primary sector 
companies experience financial distress. 
Financial distress in primary sector companies 
is triggered by a high debt to asset ratio. This 
condition was triggered by improper debt 
management, so the risk became higher 
without being balanced by a high rate of 
return. The results of the research of Nurhayati 
et al., (2017) state that the debt to asset ratio 
has an impact on the occurrence of financial 
distress. This research is also supported by 
research that has been done by Sasongko et 
al., (2021) which says that debt to assets has 
an impact on financial distress. From this 
explanation, the hypothesis used is: 
H2: Debt to asset ratio has an effect on 
financial distress. 

Effect of return on assets on financial 
distress 

Return on assets is an indicator that shows 
the capability of all assets to generate profits 
(Agustini & Wirawati, 2019). When the 
company decides to allocate an asset, it is 
necessary to calculate the return that will be 
obtained. If the allocation of assets only 
produces low returns, it will result in a smaller 
return on assets. 

In signaling theory the company can 
provide a signal from the published financial 
statements, a positive signal of a company can 
be seen from the high return on assets value 
and conversely a low return on asset value 
can give a negative signal. It can be concluded 
that a large return on assets indicates that the 
financial condition is in a healthy condition. 



 
 
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In research conducted by Iswari and 
Nurcahyo (2020) there is evidence that the 
return on assets ratio has an impact on the 
emergence of financial distress. This research 
is also supported by research conducted by 
Silvia and Yulistina (2022) which states that 
return on assets has an impact on financial 
distress. Based on this explanation, the 
hypotheses used are: 
H3: Return on assets can affect the state of 
financial distress. 

Effect of tobin's q on financial distress 
Tobin's q ratio is the ratio used in assessing 

a company. The higher Tobin's Q ratio is an 
indication of the higher incentives to build 
assets for speculative purposes (Curto, 2020). 
A large Tobin's q ratio indicates a company is 
considered valuable by the market, thus 
indicating the company is in an optimal 
condition and has good possibilities in the 
future. 

Tobin's q ratio can be used as a signal for 
users of financial statements. The higher the 
value of Tobin's q means the company is in a 
healthy condition. In signaling theory, the 
larger Tobin's q value can give a positive 
signal and vice versa where the lower Tobin's 
q value can give a negative signal about the 
condition of the company. 

The results of research conducted by 
Kazemian et al., (2017) say that the Tobin's Q 
ratio has an influence on the occurrence of 
financial distress. In his research, it is also 
stated that the lower the Tobin's Q value, the 
greater the possibility of financial distress. 
From this explanation, the hypothesis used: 
H4: Tobin's Q can affect financial distress. 

 
RESEARCH METHODS 

The approach used in this research is a 
quantitative approach which is measured 
using numbers and statistical procedures. 
What is meant by quantitative research is 
research based on the philosophy of 
positivism whose data analysis is 
quantitative/statistical (Saragih et al., 2021). 

The research method used in this research 
is used to obtain information about financial 
distress. With this research, it will be known 
the relationship between the current ratio, 
return on assets, debt to assets, and tobins'q 
on financial distress in a company, especially 
those engaged in the hospitality service 
sector. 

 

Population and sampling techniques 
 The population used in this research 

is a public entity that runs in the field of 

hospitality services and is registered in the 
Sharia Securities List. The sampling technique 
used is purposive sampling technique. Only 
entities that meet the requirements will be the 
sample in this study. With the condition that 
the company must submit a report for the first 
quarter of 2020 until the third quarter of 2021. 
The population in this study is as follows: 

 
Table 1. Hotel Service Sector Companies 

No. Company name Code 

1. PT. Indonesian Paradise Property Tbk INPP 

2. PT. Hotel Sahid Jaya International Tbk SHID 

3. PT. Jakarta Setiabudi International Tbk JSPT 

4. PT. Red Planet Indonesia Tbk PSKT 

5. PT. IsIand Concepts Indonesia Tbk ICON 

6. PT. Hotel Fitra International Tbk FITT 

7. PT. Planet Properindo Jaya Tbk PLAN 

8. PT. AndaIan Perkasa Abadi Tbk NASA 

9. 
PT. Graha Lestari Indah Tbk 
Development 

PGLI 

10
. 

PT. Jakarta International HotelIs & 
Development Tbk 

JIHD 

11
. 

PT. Bukit UIuwatu VIIa Tbk BUVA 

Source: OJK Board of Commissioners Decree No. 
KEP-63/D.04/2020 Regarding List of Sharia 
Securities 

 
Of these companies that meet the sample 

criteria, only 8 companies, this is because 
there are several entities that do not meet the 
purposive sampling requirements, namely the 
company PT. Bukit UIuwatu VIIa Tbk. with 
BUVA code. After searching the company's 
website and also the website of the Indonesian 
stock exchange, annual financial statements 
were not found for 2020. The company's 
status is still suspended based on the 
announcement of the temporary suspension 
of securities trading number Peng-SPT00006/ 
BEI.PP1 /07-2021, Peng-SPT-00012 
/BEI.PP2/07-2021, and Peng-SPT-
00009/BEI.PP3/07-2021 dated July 16, 2021. 
In addition, PT. Planet Properindo Jaya Tbk. 
with the PLAN code also did not meet the 
requirements because they did not submit 
financial statements for the first quarter to the 
third quarter of 2020 and PT. Hotel Sahid Jaya 
International Tbk. have not submitted their 
financial statements for the third quarter of 
2021. Of the eleven companies, only eight met 
the purposive sampling requirements. The 
total sample in each variable is 56 originating 
from the financial statements of the first 
quarter of 2020 to the third quarter of 2021. 



 
 
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List of entities that have met the purposive 
sampling requirements are as follows: 

Table 2. Sample of Hotel Service Sector 
Companies 

No. Company name Code 

1. PT. Indonesian Paradise Property Tbk INPP 

2. PT. Jakarta Setiabudi International Tbk JSPT 

3. PT. Red Planet Indonesia Tbk PSKT 

4. PT. IsIand Concepts Indonesia Tbk ICON 

5. PT. Hotel Fitra International Tbk FITT 

6. PT. AndaIan Perkasa Abadi Tbk NASA 

7. PT. Graha Lestari Indah Tbk Development PGLI 

8. 
PT. Jakarta International HotelIs & 

Development Tbk 
JIHD 

Source: OJK Board of Commissioners Decree No. 
KEP-63/D.04/2020 Regarding List of Sharia 
Securities 

 
Hotel Fitra International with the code FITT 

is a company established with the deed of 
establishment no. 34 dated January 24, 2014 
and domiciled in Majalengka, previously this 
company was named PT. Hotel Fitra Syariah. 
On June 11, 2019, this company was officially 
listed on the Indonesia Stock Exchange. Hotel 
Fitri International has a subsidiary, namely PT. 
Bumi Majalengka Permai and PT. Fitra 
Amanah Wisata. 

Jakarta International Hotel & Development 
was established under the name PT. Djakarta 
International Hotel. The Company was later 
changed to PT Jakarta International Hotel 
based on the Deed of Amendment to the 
Articles of Association No. 41 dated June 8, 
1973. The Company was established in 1969 
to continue the construction of the Borobudur 
Inter-Continental Hotel (now known as Hotel 
Borobudur Jakarta - HBJ), as well as to take 
over its ownership from the previous owner PT 
Perhotelan Banteng Baru. Hotel Borobudur 
Jakarta has a 19-storey building with 866 
rooms that stands on a land area of 89,510 m 
2. Hotel Borobudur Jakarta also has serviced 
apartments "Garden Wing" with a capacity of 
140 units. 

The company turned into a public company 
by listing shares on February 29, 1984 on the 
Jakarta Stock Exchange with a total of 
6,618,600 shares (42.02% of the issued and 
fully paid capital) with a par value of Rp 1,000 
per share and an offering price of Rp 1,500. 
The Company has a subsidiary under the 
name PT. Danayasa Arthatama, the 
company's business business is increasing in 

real estate (mall) along with the operation of 
Pacific Place Jakarta (integration of Pacific 
Place, One Pacific Place offices, The Ritz-
Carlton Pacific Place and Pacific Place 
Residence Apartment) in SCBD in 2007. Then 
in 2007, through PT Pacific Place Jakarta (a 
subsidiary of PT Danayasa Arthatama) 
operates The Ritz-Carlton Pacific Place and 
The Langham Jakarta, which are the 
integration of three new office buildings, 
namely Revenue Tower, Treasury Tower and 
Prosperity Tower. 

PT Jakarta Setiabudi International Tbk. 
was established based on the deed of 
establishment No. 4 dated July 2, 1975 the 
company is engaged in the real estate 
investment business which concentrates on 
property and hospitality. In 1998, the 
Company became a public company after 
conducting an initial public offering on the 
Indonesian stock exchange with the stock 
code JSPT. Subsequently, in 2002, the 
company conducted a limited public offering I 
in the context of Pre-emptive Rights. Several 
hotels that are subsidiaries of Jakarta 
Setiabudi Internasional are PT Hotel 
Investama Realty, PT Bangun Hotel 
Nusantara, PT Hotel Kemang Realty, PT Hotel 
Yogya Realty, PT Hotel Cikini Realty, PT Hotel 
Pekalongan Realty, PT Bali Nusadewata 
Village, PT Belitung Resort Internasional, and 
PT Natuna Resort International. 

Companies that are sampled in this study 
are companies registered in the list of sharia 
securities. If the company is in the list of sharia 
securities, it can be ascertained that the hotel 
company has fulfilled the requirements not to 
carry out activities and types of business that 
are contrary to sharia principles, one of which 
is the sale of haram goods or services not 
because of the substance (haram li-ghairihi) 
stipulated by the National Sharia Council - 
Indonesian Ulema Council and goods or 
services that damage morals and are harmful. 
Apart from the aspect of the object of business 
activity, the company must also meet financial 
requirements, namely total interest-based 
debt compared to total assets of not more than 
45% and total interest income and other non-
halal income compared to total operating 
income and other income of not more than 
45%. than 10%. 

 

Types and sources of data 
The data that will be used in this research 

is the type of quantitative data. Quantitative 
data used are financial data for 2020 and 2021 
issued by companies which are available on 



 
 
JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 4, 2022 pp. 34-52 

 

the company's website and the Indonesian 
stock exchange website. The data sources 
used are secondary data sources from 
published financial reports. 

 

Variable operationalization 

Independent variable 
The independent variables in this research 

are the current financial ratio, debt to asset 
ratio, return on assets and Tobin's Q. The data 
will be obtained from secondary data from the 
company's financial statements. 
 

Table 3. Variable Formula 

No. Variable Formula 

1. Current Ratio 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡

𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠
 

2. Debt to Asset 
Ratio 

 

𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡
 

3. Return on Assets 
 

𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡
 

4. Tobin's Q 
 

𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒 + 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠

𝐴𝑠𝑠𝑒𝑡
 

 
Market Value =   Share 𝑥 𝑃𝑟𝑖𝑐𝑒 

Source: (Mediyanti et al., 2021; Ramadhanty & 
Sukmaningrum, 2020; Sumani, 2020) 

 

Dependent variable 
Dependent variable is financial distress 

which is proxied by Altman Z score. The data 
can be obtained through analysis of data from 
the company's financial statements. Altman Z 
score is used to determine the level of financial 
distress in a company. The first Altman z score 
model was first coined by Altman 1968. Goh 
et al., (2021) in their research stated as 
follows: 

“Altman's z-model is a simple bankruptcy 
prediction model, yet largely accurate, and can 
be used by individuals who have limited or no 
in-depth exposure to accounting or finance to 
assess business continuity of a firm.” 

Based on this statement, it can be 
concluded that the financial distress 
assessment method using the Altman z score 
is still relevant and accurate. Altman z score 
formula quoted from the research of Goh et al., 
(2021) as follows : 

Z = 3.3 (X1) + 0.999 (X2) + 0.6 (X3) + 1.2 (X4) + 
1.4 (X5) 
X1 = EBIT/total assets 
X2 = Net revenue/total assets 
X3 = Market cap/total liabilities 
X4 = Working capitalI/total assets 
X5 = Retained earnings/total assets 

According to Goh et al., (2021) “a healthy 
z-score is usually more than 2.99, while a 
score between 1.80 and 2.99 is normally 
categorized as “undetermined” where the 
model could not determine the financial health 
status of a firm. "A healthy z-score is usually 
above 2.99, while a score of 1.80 to 2.99 is 
usually categorized as "undetermined" which 
cannot explain the company's financial health 
status." 

Data analysis techniques 
Data analysis techniques used include 

descriptive statistical analysis, classical 
assumption test, panel data regression 
analysis, and hypothesis testing. In data 
analysis, Eviews 12 software is used , Eviews 
12 is used because the software is more 
appropriate when used for panel data model 
research when compared to other software 
such as SPSS and MiniTab, especially on 
macroeconomic data (Santoso, 2018:7). 
According to Juanda (2021) advantages of 
Eviews compared to other econometric 
application programs is its completeness in 
providing equipment for time series 
econometrics and panel data processing. 
Eviews analytical skills include synthetic data, 
financial analysis, cost analysis and 
forecasting (Kusumaningtyas et al., 2022:11). 

Descriptive statistics 
According to Wahyuni (2020) descriptive 

statistics are activities to calculate the mean, 
median, mode, and standard deviation. 
Descriptive statistics is a part of statistical 
science, which is related to data collection 
which aims to explain the data displayed 
numerically (data distribution, placement size, 
data deviation and frequency distribution) or in 
the form of graphics (graphs and 
diagrams/tables) to make it easier to 
understand. 

Panel data regression analysis 
According to Juanda (2021:6) the 

combination of time series and cross section 
data is panel data, ie data obtained from the 
cross section is examined by repeating with 
the same individual unit but at different times. 
Panel data regression is a combination of data 
from cross section and time series data where 
there are individual/object differences in a time 
span. 

Panel data regression equation model 
which is a combination of cross section data 
and time series data Juanda (2021:178) as 
follows: 

 



 
 
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Yit = α + β1X1it + β2X2it + … + βnXnit + e  
Yit = dependent variable (dependent) 
α  = Constant 
β1    = Regression coefficient 
Xit  = Independent variable 
i  = Entity-i  
t  = Period-t 

  

Panel data model estimation 
According to Mire (2022:167) there are 

three estimation techniques for panel data 
regression models. The models are the 
common effect model, fixed effect, and 
random effect model. 
Common effect model 

This technique is a simple technique for 
measuring models in the form of panel data 
(Kusumaningtyas et al., 2022:20). 
Measurements using the common effect 
model are carried out in a combined way 
between cross-section and time series but do 
not take into account differences in time and 
entities. The most commonly used approach is 
the Ordinary Least Square (OLS) method. The 
Common Effects model does not take into 
account differences in individual time and size, 
i.e. the behavior of the data between 
individuals is the same over different time 
periods. 
Fixed effect model 

The effects model approach still uses the 
assumption that the intercept force for each 
individual is different but the gradient between 
individuals remains. In this technique, dummy 
variables are used to define interindividual 
differences in intercepts. Intercept differences 
can be in the form of work culture, incentives, 
and managerial (Kusumaningtyas et al., 
2022:20). 

 
 

Random effect model  
According to Kusumaningtyas et al., 

(2022:20) the random effect model is suitable 
for data with high complexity. The approach 
used in the random effect is assumed that 
each company has a different intercept where 
the intercept is a random variable. This model 
is useful if the sampled companies are 
randomly selected and represent the 
population. 

Data analysis stage 
In determining/determining the right 

method in using panel data, how many tests 
can be done. The selection or determination of 
the best estimation technique between the 
common effect, fixed effect, and random effect 
model is the Chow test and the Hausman test 

(Mire, 2022:172). Chow test is used to see 
whether panel data regression using the fixed 
effect model is more appropriate than panel 
data regression that does not use dummy 
variables/ common effect method. Hausman 
test used in determining whether the fixed 
effect model is better to use than the random 
effect model. 

Classical assumption test 
In order to achieve the objectives of this 

research, first it is necessary to carry out a 
classical assumption test, this test is used to 
provide certainty whether the panel data 
regression model used has met the 
requirements of normality, multicollinearity, 
and heteroscedasticity. If all the classical 
assumptions are met, it means that the 
analytical model can be used. Classical 
assumption test was run to get reliable 
regression results. However, specifically in 
panel data regression research, only a few 
classical assumption tests are needed, 
namely multicollinearity and normality. 
Normality test 

According to Hadi Ismanto and February 
(2021:59) the normality test is intended to 
determine whether the residuals are normally 
distributed, or in other words, the research 
data is normally distributed or not. The 
normality test is used to see whether the 
variables in the research are normally or not 
normally distributed. 
Multicollinearity test 

The multicollinearity test is a classic 
assumption test used to see whether there is 
a correlation between independent variables. 
To perform the multicollinearity test, it can be 
done with the VIF test and the correlation test 
(Hadi Ismanto & February, 2021:67). 

 
 

Hypothesis test 
Hypothesis testing is used in assessing the 

significance of the regression coefficients. 
Therefore, the regression coefficient needs to 
be tested. There are several kinds of 
hypothesis testing on the regression 
coefficient, namely: 

T test  
According to Hadi Ismanto and February 

(2021:137) the t-test is used to check whether 
the parameters used to estimate the 
regression model equation are the right 
measurements. If the f-test is used to check 
the regression coefficients simultaneously, 
then the t-test is used to check the regression 
coefficients individually. If the probability value 
is less than 0.05, it can be said to have a 



 
 
JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 4, 2022 pp. 34-52 

 

significant effect and vice versa if the 
probability value is higher than 0.05, it means 
that it has no effect. 
Coefficient of Determination 

The coefficient of determination is a 
coefficient that shows the variation in the 
impact of an independent variable on the 
dependent variable (Hadi Ismanto & February, 
2021:138). The coefficient of determination or 
often referred to as goodness of fit is explained 
by R-square, which is one of the 
measurements needed in the regression 
model, this is because R-square is able to 
provide information on the feasibility of an 
estimated regression model. The value of the 
coefficient of determination shows the amount 
of variation in the dependent variable can be 
explained through the independent variable. 
 

RESULTS AND DISCUSSION  

Descriptive statistical analysis  
Descriptive statistical analysis is used in 

research to produce a description of the data 
or descriptive. Descriptive statistical analysis 
in this research displays the number of 
observations, max value , mean and standard 
deviation of each variable used in a research. 
The display of descriptive statistics on each 
research variable is presented in table 4 
below: 

Table 4. Descriptive Statistics 

No. Variabel Mean Maximum Minimum 
Std. 
Dev. 

1. Z 3.293 2.870 -0.336 5.076 

2. ROA -0.019 0.075 -0.139 0.036 

3. Q 1.081 2.465 0.425 0.468 

4. DAR 0.298 0.595 0.049 0.149 

5. CR 2.068 8.500 0.168 1.775 

Source: Data processed Eviews 12, 2022 

Based on the results of descriptive 
statistics in table 4 the following conclusions 
are obtained: 
1. Financial distress variable has a min value 
of -0.336 and has a max value of 2.870 The 
average value (mean) with a size of 3.293 thus 
means that most hotel sector companies do 
not have serious financial problems. Financial 
distress has a standard deviation of 5,076 the 
number is greater than the average value 
(mean) is 3,293. Based on the data, it can be 
interpreted that the average value of financial 
distress as measured by the Altman z score 
has a high deviation. 
2. Return on asset variable has a min value of 
-0.13 9 and has a max value of 0.075. The 
average number (mean) is -0.019. Return on 
assets has a standard deviation of 5.06 3 , this 

figure is more than the average (mean) which 
is -0.019. Based on these data, it can be 
concluded that the average return on assets 
variable has a high deviation rate. 
3. Tobin's q ratio variable has a min value of 
0.425 and a max value of 2.465. The average 
number (mean) is 1,081 which indicates that 
the hotel sector company has a high number 
of companies, which is greater than the book 
number. Tobin's q ratio has a standard 
deviation of 0.468, this number is smaller than 
the average (mean) of 1.081. Based on these 
data, it can be interpreted that the average 
Tobin's q ratio has a low deviation. 
4. Debt to asset variable has a min value of 
0.049 and a max value of 0.595. The average 
number (mean) is 0.298. Debt to assets has a 
standard deviation of 0.149, which is smaller 
than the average (mean) of 0.298. Based on 
these numbers, it can be concluded that the 
average debt to asset has a low deviation. 
5. Current ratio variable has a min value of 
0.168 and a max value of 8,500. The mean 
(mean) is 2.068. The current ratio has a 
standard deviation of 1.775, which is smaller 
than the mean (mean) of 2.068. Based on 
these data, it can be interpreted that the 
average current ratio has a low deviation. 
 

Test of panel data model estimation 

Common effect model 
Common effect model technique is a 

simple model used in estimating the 
measurement of the panel data model, 
through the use of the assumption that the 
intercept and slope are constant over time 
periods and units. (Individual). Common Effect 
models are presented in the following table. 

Table 5. Common Effect Model 
 

No. Variable Coefficient Std. 

Error 

t-

Statistic 

Prob. 

1. C 1.445 1.236 1.168 0.248 

2. ROA -1.870 1.029 -1.816 0.075 

3. Q 2.413 0.731 3.298 0.002 

4. DAR -1.469 2.599 -5.652 0.000 

5. CR 1.578 0.223 7.064 0.000 

Source: Data processed Eviews 12, 2022 

 

Fixed effect model 
fixed effect model approach uses the basic 

assumption that the intercept for each 
individual is different and the slope between 
individuals is the same. The fixed effect model 
is presented in the following table: 

 
 



 
 
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Table 6. Fixed Effect Model 
 

No. Variable Coefficient Std. 
Error 

t-
Statistic 

Prob. 

1. C 2.334 1.868 1.249 0.218 

2. ROA 2.990 6.360 0.470 0.641 

3. Q 6.474 0.744 8.700 0.000 

4. DAR -2.607 6.748 -3.864 0.000 

5. CR 0.868 0.187 4.640 0.000 

Source: Data processed Eviews 12, 2022 

 

Random effect model 
The approach used in random effects uses 

the assumption that each research object has 
a different intercept and the intercept is a 
random/stochastic variable. The random 
effect model is presented in the following 
table: 

 
Table 7. Random Effect model 

 

No. Variable Coefficient 
Std. 

Error 
t-

Statistic 
Prob. 

1. C 0.665 1.502 0.442 0.660 

2. ROA 2.625 6.259 0.419 0.677 

3. Q 5.656 0.659 8.580 0.000 

4. DAR -1.839 4.261 -4.317 0.000 

5. CR 0.991 0.177 5.592 0.000 

Source: Data processed Eviews 12, 2022 

 

Data analysis test 
There are several tests to determine the 

best panel data regression model, whether 
common effect , fixed effect or random effect. 
The following are the tests carried out for the 
selection of panel data regression models. 

Chow test 
This test is used to see whether the panel 

data regression technique using the fixed 
effect method will more precise than 
regression using the common effect method. 

 
Table 8. Chow Test 

 

No. Effects Test Statistic d.f. Prob. 

1. Cross-section F 24.857 -7,44 0.000 

2. 
Cross-section 

Chi-square 
89.617 7 0.000 

Source: Data processed Eviews 12, 2022 
 

If the p-value < 0.05 then the fixed effects 
model is more appropriate to use than the 
common effects (Kusumaningtyas et al., 
2022:105). Based on table 8, it can be seen 
that the probability of showing the number 

0.000 means that the probability value is less 
than 0.05 and it can be concluded that the 
fixed effect model is the right model. The 
model chosen is fixed effect, so it is necessary 
to test Hausman to ascertain whether the fixed 
effect model is more appropriate than the 
random effect method model. 

Hausman test 
The Hausman test is a test method to 

ensure that the fixed effect model is more 
appropriate than the random effect model.  

 
Table 9. Hausman Test 

 

No. Test Summary 
Chi-Sq. 
Statistic 

Chi-
Sq. 
d.f. 

Prob. 

1. 
Cross-section 

random 
9.293 4 0.054 

Source: Data processed Eviews 12, 2022 
 

Conclusions in the Hausman test are used 
as guidelines if the probability value > 0.05 
means that a better model is used, namely the 
random effect model. If the probability value is 
< 0.05, it means that the most appropriate 
model to use is the fixed effect model 
(Kusumaningtyas et al., 2022:105). 

The output results from the Hausman test 
table above show the probability value of the 
Hausman test of 0.05 4 which is greater than 
0.05 so that it can be concluded that the most 
appropriate model used in this study is the 
random effect model rather than the fixed 
effect model. The method chosen is random 
effect, it is necessary to test the lagrange 
multiplier to determine whether the random 
effect method is more appropriate than 
regression using the common effects method. 

Lagrange Multiplier Test 
This test is used to determine whether the 

panel data regression technique using the 
random effect method will more precise than 
regression using the common effect method. 

 
Table 10. Lagrange Multiplier Test 

  
No. 

 
Test Hypothesis 

 
Cross-
section 

Time Both 

1. Breusch-
Pagan 

48.001 0.104 48.106 

2. 
 

(0.000) (0.746) (0.000) 

3. Honda 6.928 -0.323 4.671 

4. 
 

(0.000) (0.626) (0.000) 

5. King-Wu 6.928 -0.323 4.469 

6. 
 

(0.000) (0.626) (0.000) 



 
 
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7. Standardized 
Honda 

10.421 -0.085 3.077 

8. 
 

(0.000) (0.533) (0.001) 

9. Standardized 
King-Wu 

10.421 -0.084 2.792 

10. 
 

(0.000) (0.53) (0.002) 

11. Gourieroux, 
et al. 

-- -- 48.001 

12. 
   

(0.000) 

Source: Data processed Eviews 12, 2022 

 
If the Breush-Pagan p-value < 0.05, it 

means that the random effects model is more 
appropriate than regression using the 
common effect method (Kusumaningtyas et 
al., 2022:105). The output results from the 
Lagrange multiplier test table above show the 
Breush-Pagan probability value of = 0.000 
which is smaller than 0.05 so that it can be 
concluded that the most appropriate model 
used in this study is the random effect model 
rather than the common effect model. 
 

Classical assumption test 

Normality test 
Normality test is one of the classical 

assumption tests used in determining a 
dependent and independent variable having 
normal or abnormal distribution observations. 
The results of the normality test can be seen 
in the following figure: 

  

0

2

4

6

8

10

-4 -2 0 2 4 6

Seri es : Standardi zed Res iduals

Sa mpl e  2020Q1 2021Q3

Obs e rva ti ons  56

Me a n       2.04e -15

Me di a n   1.146068

Ma xi mum  6.600457

Mi ni mum -5.105303

Std. De v.   2.683586

Ske wne s s   -0.185895

Kurtos i s    2.028657

Ja rque -Be ra  2.524048

Proba bi l i ty  0.283080  
Figure 2. Normality Test 

Source: Data processed Eviews 12, 2022 

 
The normality test in this research uses the 

jarque-fall method. The data can be said to be 
normal if the p-value is > 0.05 
(Kusumaningtyas et al., 2022:64). Based on 
Figure 2, it can be seen that the results of the 
normality test show that the probability value 
shows the number 0, 283 and the number is 
greater than the number 0.05, so from the 
value 0, 283 > 0.05, it can be concluded that 
the data is normally distributed. According to 
Pallant (2007) in Viana et al., (2021) the data 
“Pallant states that studies with a large sample 

of more than 40 samples can ignore the 
problem of normality because a large sample 
can follow the population distribution." The 
sample used in this study amounted to 56 and 
the jarque fallow value was above 0.05, so it 
can be concluded that the data is normal. 

Multicollinearity test 
In multicollinearity testing, it is intended to 

explain that in the regression model there is a 
correlation between independent variables. 
The right regression model should not have a 
correlation between the independent 
variables. The variable has multicollinearity 
problems if the correlation between X1 and X2 
exceeds 0.08 (Kusumaningtyas et al., 
2022:68). The results of the multicollinearity 
test are presented in the following table. 

 
Table 11. MuIticoIIinearity Test 
 

No.  Z ROA Q DAR CR 

1. Z 1 0.206 0.438 
-

0.691 
0.797 

2. ROA 0.206 1 -0.199 
-

0.428 
0.359 

3. Q 
0.438 

 
-0.199 1 

-
0.089 

0.273 

4. DAR -0.691 -0.428 -0.089 1 -0.539 

5. CR 0.797 0.359 0.273 
-

0.539 
1 

Source: Data processed Eviews 12, 2022 
 

Based on the results of the multicollinearity 
test above, it shows that there is no large 
correlation value between the independent 
variables, namely with a value that does not 
exceed 0.8. With a value below 0.8, it can be 
concluded that from these results there is no 
data that has multicollinearity. 

 
Panel data regression analysis 

Based on the panel data regression model 
using the Eviews program ( common effect 
model, fixed effect model, and random effect 
model ) and the tests carried out ( chow test , 
hausman test , and lagrange multiplier test ) it 
can be concluded that the most appropriate 
regression model is used for This research is 
a random effect model. The results of the fixed 
effect model panel data regression can be 
seen in the following table: 

 
Table 12. Random Effect model 

 

No. Variable Coefficient 
Std. 

Error 
t-

Statistic 
Prob. 

1. C 0.665 1.502 0.442 0.660 

2. ROA 2.625 6.259 0.419 0.677 

3. Q 5.656 0.659 8.580 0.000 



 
 
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4. DAR -1.839 4.261 -4.317 0.000 

5. CR 0.991 0.177 5.592 0.000 

Source: Data processed Eviews 12, 2022 

 
Research using panel data regression was 

conducted to see the impact of the 
independent variable on the dependent 
variable. The results of panel data regression 
in the fixed effect model show the results of the 
variable current ratio, debt to asset ratio, 
return on assets and Tobin's q on financial 
distress , which in this case financial distress 
is measured using the Altman z score. The 
panel data regression equation is as follows: 

 
Z = 0.665 + 2,625ROA + 5.656Q + (-
1.839)DAR + 0.991CR + e 

 
From the regression can be explained as 

follows: 
1. Constant coefficient of 0.665 and positive 
value, it can be concluded that Y = Z score will 
have a value of 0.665 if the current ratio, debt 
to asset ratio, return on assets and Tobin's q 
each have a value of 0. 
2. Variable return on assets (ROA) has a 
regression coefficient of 2.625 states that each 
addition of the return on asset variable is 1%, 
and it is assumed that other variables are the 
same, it can increase the z score by 2.625. 
3. The regression coefficient of the Q ratio 
shows a number of 5.656, which means that 
every 1% increase in the Q ratio variable, and 
it is assumed that other variables are fixed, it 
will increase the z score by 5,656. 
4. The variable debt to asset ratio (DAR) has 
a regression coefficient of -1.839 that every 
increase in the debt to asset ratio variable is 
1%, and it is assumed that other variables are 
fixed, it will reduce the z score by -1,839. 
5. Current ratio regression coefficient has a 
number of 0.991 , which means that each 
addition of the current ratio variable is 1%, 
assuming other variables are fixed, it will 
increase the z score by 0.991. 

 

Hypothesis test 
T test  

T test is used in regression testing. The 
results of the t-test test are concluded using 
the t-statistical value and probability value, 
whether it is above 0.05, which means that the 
independent variable has no effect on the 
dependent variable. If the test number shows 
a probability number below 0.05 which means 
that the independent variable has an influence 
on the dependent variable. Based on table 

4.12 the T hypothesis test obtained the 
following research results: 
1. First hypothesis (H1) current ratio has an 
influence on the state of financial distress. 
Based on the results of the current ratio 
regression , it produces a probability value of 
0.000 < 0.05 and the t statistic value is 5. 592. 
The current ratio variable has a probability 
value below 0.05 which means that the current 
ratio has an influence on financial distress. 
2. The second hypothesis (H2) debt to asset 
ratio has an effect on financial distress. Based 
on the results of the debt to asset ratio 
regression, it produces a probability value of 
0.00 0 < 0.05 and the t statistic value is -4.3 
17. From these data, it can be concluded that 
the debt to asset ratio has a negative effect on 
financial distress. 
3. The third hypothesis (H3) returns on assets 
can affect the state of financial distress. Based 
on the results of the regression return on 
assets produces a probability value of 0.6 77 
> 0.05 and a statistical t value of 0.419. A 
value greater than 0.05 can be concluded that 
the return on assets has no effect on financial 
distress. 
4. The fourth hypothesis (H4) tobin's q has an 
influence on financial distress. Based on the 
results of Tobin's q regression, it produces a 
probability value of 0.000 < 0.05 and the t 
statistic value is 8. 580. Based on these data, 
it can be concluded that Tobin's q has an 
influence on financial distress. 

Coefficient of Determination Test 
Testing the coefficient of determination / r-

square is a number that shows the size of the 
capability of the independent variable to 
explain the dependent variable. The 
magnitude of the value of r 2 is between zero 
and one (0 < R < 1). If the value shows a 
number that is getting closer to one, then the 
regression model can be said to be good. 
Based on table 4.10 the value is 0.795 , it 
means that 79.5 % financial distress can be 
explained through the variables current ratio, 
debt to asset ratio, return on assets and 
Tobin's q. While 20 0. The other 5 % is 
explained by variables other than this research 
model. 

 

Discussion 

Effect current ratio to financial distress 
ratio is an indicator that shows the 

company's capability to settle short-term 
liabilities using current assets (Kazemian et 
al., 2017). The results of this research show 
that H1 is accepted, namely the current ratio 
has a positive influence on financial distress. 



 
 
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These results are supported by a probability 
value of 0.000 < 0.05 and the t statistic value 
is 5. 592. 

These results are consistent with the 
research of Nurhamidah and Kosasih (2021) 
which shows that the current ratio has an 
effect on financial distress. The study also 
explained that the influence of the current ratio 
on financial distress means that the smaller 
the value of the current ratio , the greater the 
possibility of financial distress. 

This study is in line with the findings of 
Nurhidayah and Rizqiyah (2017) which show 
that liquidity measured using the current ratio 
has an effect on financial distress. The 
influential results in this study are because the 
company does not have enough funds to meet 
its obligations, so it takes a long time to 
liquidate other assets.  

The current ratio is one way to assess the 
level of liquidity of a company and the 
liquidation of a company can be used as a 
signal for users of financial statements. High 
liquidity can give a positive signal to investors 
and creditors, with high liquidity the company 
is considered to have the ability to meet 
current obligations and is capable of managing 
the company (Agustini & Wirawati, 2019). 
signaling theory is used to explain that the 
current ratio can provide a signal for users of 
financial statements, where the higher the 
current ratio value means the better the 
financial condition of a company. 

influence means that the higher the current 
ratio , the higher the z score, which means that 
the possibility of financial distress will be 
lower. The current ratio tall is a good indicator 
for the company. A hotel company with a high 
current ratio means that it has the ability to 
meet its current liabilities so that later it can 
reduce the occurrence of financial distress. 

Influence debt to asset ratio against 
financial distress 

The amount of debt in a company can lead 
to high interest expenses, and can lead to 
financial distress (Iswari & Nurcahyo, 2020). 
one of the characteristics of the emergence of 
financial distress namely a condition where the 
company does not have the ability to settle its 
obligations. The debt to asset ratio (DAR) is a 
comparison between total debt and total 
assets and is used in assessing solvency, in 
other words, the amount of company assets 
financed by debt (Sumani, 2020). The results 
of this study show that H 2 is accepted, namely 
the debt to asset ratio has a negative effect on 
the emergence of financial distress, this is 

evidenced by the sig value of 0.00 0 < 0.05 
and the t statistic value is -4.3 17. 

With signaling theory the debt to asset ratio 
is used as a signal to users of financial 
statements. The smaller the value of the debt 
to asset ratio can be a positive signal, this is 
because the value of the debt to asset ratio will 
be better the lower it is. Debt to assets is a 
ratio that assesses a company 's assets 
financed by debt, so if the debt to asset ratio is 
higher, it means that more assets are financed 
through debt. 

This study is consistent with the findings of 
Nurhayati et al., (2017) which shows that the 
debt to asset ratio has an effect on financial 
distress. The study also explained that the 
higher the value of the debt to asset ratio , the 
greater the company experiencing financial 
distress. The cause of financial difficulties is 
the increasing burden of the company in 
settling its liabilities and interest. 

The research results of Sasongko et al. 
(2021) also show that the debt to asset ratio 
has an influence on financial distress. In this 
study, it is explained that if the company's 
financing is mostly sourced from debt, then the 
company will have difficulty paying future 
liabilities. The difficulty is due to the greater the 
amount of debt than assets. 

The negative effect on the emergence of 
financial distress occurs when the higher the 
debt owned by a company. The higher the 
debt, but not followed by higher income, the 
company will find it difficult to pay its 
obligations, thereby leading the company to a 
state of financial distress. 

The decline in hotel company revenues 
occurred as a result of the COVID-19 
pandemic resulting in a decrease in the 
company 's ability to pay debts and interest 
and bringing the company closer to financial 
distress. Having a negative effect means the 
lower the debt to asset ratio , the higher the z 
score , which means the healthier the 
company's finances are. 

Influence Return on Assets Against 
Financial Distress 

Return on assets is an indicator that shows 
the capability of all assets to generate profits 
(Agustini & Wirawati, 2019). The results of the 
study show that return on assets has no effect 
on financial distress , it is evidenced by the 
probability value of 0.6 77 > 0.05 and the t 
statistic value of 0.419. With a significance 
value above 0.05, it can be concluded that the 
research results reject H 3. The results of the 
study prove that return on assets has no effect 
on financial distress. This research contradicts 



 
 
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the research conducted by Iswari and 
Nurcahyo (2020) , which shows that there is 
evidence that the return on assets ratio has an 
influence on the incidence of financial distress.  

Low profitability is a signal of the 
company's limitations in converting revenue 
streams into profits, so the higher the 
profitability, the lower the possibility of 
financial distress (Kazemian et al., 2017). This 
condition is because the high return on assets 
has an indication that the financial condition is 
in a healthy condition. Return on assets is a 
ratio used to assess the ability of a company 
to earn a profit (Daryanto et al., 2019). 

The results of this study are in line with 
research Maulana and Suhartati (2022) who 
show that return on assets has no effect on 
financial distress. In signaling theory , the 
company can provide signals from the 
published financial statements. One of the 
positive signals of a company can be seen 
from the value of high return on assets , but 
based on research results that have no effect 
on financial distress , if the return on assets is 
high, the positive signal obtained is only in the 
form of improved profitability. 

Research conducted by Peluni et al., ( 
2020) shows the same result, namely that 
return on assets has no effect on financial 
distress. In the study, it was stated that if the 
company is able to manage assets effectively 
and efficiently, then the company will get 
optimal profits and will not cause financial 
distress. 

Basically ROA is a company's strength in 
generating profits through empowering the 
company's assets, properly a high ROA ratio 
should be followed by a decrease in financial 
distress. With a relationship that has no effect 
in this study, it can be explained that the 
higher/lower ROA will not have an impact on 
financial distress. 

As long as the company has sufficient 
capital to bear the risks, sufficient liquidity, and 
good cost management, the ROA ratio will not 
have a significant impact on financial distress. 
Therefore, the high and low ROA of hotel 
companies will not affect the occurrence of 
financial distress. 

Influence Tobin's Q Against Financial 
Distress 

Tobin's q ratio is a ratio used to assess the 
value of a company. The higher Tobin's q ratio 
is an indication of the higher incentives to build 
assets for speculative purposes (Curto, 2020). 
The results of panel data regression analysis 
show a probability value of 0.00 0 < 0.05 and 
the t statistic value is 8. 580. Based on this 

value, it can be concluded that H 4 is 
accepted, namely Tobin's q ratio has an effect 
on financial distress. The results of this study 
are in accordance with research conducted by 
Kazemian et al., ( 2017) which states that the 
Tobin's Q ratio has a significant relationship to 
the occurrence of financial distress. Tobin's q 
ratio can be used as a signal for users of 
financial statements. The higher the value of 
Tobin's q means the company is in a healthy 
condition. In signaling theory , a higher Tobin's 
q value can give a positive signal. 

Research conducted by Maslachah et al., 
(2017) shows that Tobin's q has an effect on 
financial distress. The study also explained 
that if the company has a high investment 
opportunity as measured by the Tobin's q 
ratio, where the stock is highly valued by the 
market, it can affect the company's sales 
growth and the company's equity will be 
valued higher. 

This study is consistent with the findings of 
Valentina and Tjhai (2020) which state that 
company performance as measured by 
Tobin's q has an influence on financial 
distress. In this study it is also stated that high 
company performance will be able to meet the 
expectations of shareholders who hope to 
make profits. High profits will also be able to 
attract new investors to invest in the company. 

Tobin's q ratio is a ratio that becomes a 
benchmark for the company's value. Based on 
the test results, this ratio has a positive effect, 
which means that the higher the Tobin's q 
value, the higher the z score , which means 
the healthier the company's finances and the 
lower the possibility of financial distress. 
 

CONCLUSION  
This study aims to determine the current 

ratio, debt to asset ratio, return on assets and 
Tobin's q in influencing the incidence of 
financial distress. which is the financial 
distress variable measured using the Altman z 
score. The research was conducted on hotel 
sector companies listed in the sharia securities 
list. The research data is obtained from 
secondary data in the form of financial 
statements for the first quarter of 2020 to the 
third quarter of 2021. Based on the results of 
the regression test, it can be concluded that 
the current ratio, debt to asset ratio, and 
Tobin's q ratio have an effect on financial 
distress , while return on assets has no effect 
on financial distress. 

 
 
 



 
 
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Limitations 
The study was conducted to determine the 

current ratio, debt to asset ratio, return on 
assets and Tobin's q in influencing the 
incidence of financial distress. Observations 
were made for hotel sector companies during 
the COVID-19 pandemic. A small number of 
observations in a short time were obtained 
only in the financial statements of 2020 and 
2021. The limitations in this study are as 
follows: 
1. The observation uses quarterly/quarterly 
financial reports, so the accuracy of the data is 
inadequate because the quarterly financial 
statements have not been audited/ unaudited. 
2. Observations using quarterly/quarterly 
financial reports, the capital expenditures 
issued by the company have not received 
optimal results and on the other hand the next 
quarter's financial statements have been 
issued. 
3. Purposive sampling requirement because 
the company was suspended from the 
Indonesia Stock Exchange as a result of not 
submitting financial statements. 
4. At the time this research was conducted, the 
company had not published its financial 
statements for the fourth quarter of 2021 
because it was still in the process of being 
produced, so that the financial statements 
were only limited to the financial statements of 
the third quarter of 2021. 
 

Suggestions 
Based on the conclusions of the research 

above, the authors have several suggestions, 
namely: 
1. It is hoped that further research can use 
audited financial statements so that the 
research data is more valid.  
2. It is hoped that further research will be able 
to use a different sample. 
3. It is hoped that further research will use 
other indicators apart from the current ratio, 
debt to asset ratio, return on assets and 
Tobin's q. 
4. It is hoped that further research can use a 
longer period than this research, in order to 
obtain more valid data and also be able to 
describe more precise results. 
 

Research implications 
The implications that the author can give 

with this research are as follows: 
1. For the Company, it is hoped that the results 
of this study can be used as an analysis in 
anticipating the occurrence of financial 
distress. 

2. For science, this research can be used as a 
reference for other researchers who will 
conduct research related to the title that the 
author is researching. 
3. For practitioners, it is hoped that it can be 
used as material for analysis by investors and 
stakeholders in assessing the financial 
distress of a company. 

 

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