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Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
135
PREDICTING FINANCIAL DISTRESS
OF PUBLIC AND NON-PUBLIC
CONSTRUCTION SUB-SECTOR
COMPANIES
Yeni Febbianti *, Andi Irfan **, Jeli Nata Liyas ***, Wellia Novita ****,
Abd. Asis ***, Febri Rahmi *
* Department of Accounting, Faculty of Economic and Social Sciences, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
** Corresponding author, Department of Accounting, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Contact details: Department of Accounting, Faculty of Economics and Social Sciences, Universitas Islam Negeri Sultan Syarif Kasim Riau,
Jl. H. R. Soebrantas No. 155 KM 18, Kel. Tuah Madani Kec. Tuah Madani, 28293 Riau, Indonesia
*** Department of Management, Sekolah Tinggi Ilmu Ekonomi Riau (STIE), Indonesia
**** Department of Accounting, Faculty of Economic and Business, Universitas Putra Indonesia YPTK Padang, Indonesia
Abstract
This study examines if there are variations among financial crisis
models. It is intended to investigate whether it has the most
significant level of accuracy in predicting potential corporate
bankruptcies. This is a quantitative study; Secondary information
from financial reports serves as the data source. The study
population is public and non-public companies in the construction
sector listed on the Indonesia Stock Exchange (IDX) for 2014–2020.
In order to obtain a sample of eight businesses, targeted selection
was used for sampling. The results of this study show that
the conditions differ from those of financial distress models for
public and non-public companies. For public companies, the most
accurate models are Grover and Lavin’s (2001), Karas and Srbová’s
(2019), Fulmer’s (1984), and Ohlson’s (1980) models proven to be
100 percent. In contrast, only Fulmer’s model is entirely applicable
to non-public companies. Forecast results and best-fit models can
provide positive information or warnings for external and internal
parties.
Keywords: Prediction Model, Financial Distress, Bankruptcy, Public
Company, Non-Public Company
Authors’ individual contribution: Conceptualization — Y.F. and A.I.;
Methodology — Y.F., A.I., J.N.L., W.N., and F.R.; Resources — Y.F. and
A.I.; Writing — Y.F., A.I., J.N.L., A.A., and W.N.; Supervision — Y.F.
and A.I.; Funding Acquisition — Y.F., A.I., J.N.L., A.A., W.N., and F.R.
Declaration of conflicting interests: The Authors declare that there is no
conflict of interest.
1. INTRODUCTION
The infrastructure sector is one of the programs
that the Indonesian government is focusing on.
The construction industry contributes to the country’s
gross domestic product (GDP) by 10% (Badan Pusat
Statistik [BPS], 2023). Infrastructure improvements
are carried out to build good and quality connectivity
and economic growth in the country. Indonesia is
the largest construction market in the Association of
South East Asian Nations (ASEAN), with Indonesia’s
contribution being over 67% (Ruhulessin & Alexander,
2021). Infrastructure sector companies must have
considerable funding to run their projects. Based on
this, public companies continue to add large
amounts of debt and are threatened with financial
difficulties, especially during the COVID-19 pandemic
(Ruhulessin & Alexander, 2021). The public companies
fell by 70% in average revenue (Mulyana, 2021).
How
to
cite
this
paper:
Febbianti, Y.,
Irfan, A.,
Liyas, J. N.,
Novita, W.,
Asis, A.,
&
Rahmi, F. (2024). Predicting financial distress
of
public
and
non-public
construction
sub-
sector
companies.
Corporate
Governance
and
Organizational
Behavior
Review,
8(2),
135–143.
https://doi.org/10.22495/cgobrv8i2p13
Copyright © 2024 The Authors
This work is licensed under a Creativ e
Commons Attribution 4.0 Internationa l
License (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
ISSN Online:
2521-1889
ISSN Print:
2521-1870
Received:
30.01.2023
Accepted:
22.04.2024
JEL Classification:
G23, M21, M41
DOI:
10.22495/cgobrv8i2p13
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
136
One of the public companies, Jakarta — PT
Waskita Karya Tbk (WSKT), through their subsidiary
PT Waskita Toll Road (WTR), transferred 55% of its
shares in the Cibitung-Cilincing toll segment to PT
Akes Pelabuhan Indonesia (API) with a transaction
value of Rp2.49 trillion. The sale reduced Wasquita’s
debt to Rp5 trillion. As a result, the concession of
the Cibitung-Cilincing toll road that spans
34 kilometres (km) is owned by PT Cibitung Tanjung
Priok Port Tollways (CTP) with 55% of its shares
owned by WTR and 45% owned by API (Daelami,
2021). Selling one or more business units indicates
signs of financial difficulties in the WSKT company.
Public companies have a higher risk in paying
debts because public companies have a pre-financing
project, so the company is paid after the project is
completed the pre-financing scheme causes public
company debt to increase. In addition, the financial
performance of the public company (WSKT) declined
due to acquisition projects, low occupancy projects,
project delays and cash flow disruptions. In contrast,
in non-public companies, financial performance
decreased due to the lack of payments for projects
and the lack of completed projects.
Companies in the building construction subsector
had significant fluctuations in D/E ratio (debt-to-
equity ratio) values between 2014 and 2020. Thus,
WSKT demonstrated D/E ratio values of 354% in 2014,
212% in 2015, 266% in 2016, 330% in 2017, 331% in
2018, 321% in 2019, and 537% in 2020. One of
the non-public companies, API, also had a fluctuating
D\E ratio: 130% in 2014, 190% in 2015, 92% in 2016,
269% in 2017, 526% in 2018, 3547% in 2019, and
843% in 20201.
Judging by the indicators, the profitability of
companies in the construction sub-sector also
fluctuated in the period 2014–2020. WSKT had
a profitability ratio of 5% in 2014, 7% in 2015, 8% in
2016, 9% in 2017 and 2018, 3% in 2019, and -59%
in 2020. A non-public company, namely API, also
had a fluctuating profitability ratio: 8% in 2014, 3%
in 2015, 4% in 2016, 5% in 2017, 1% in 2018, -29%
in 2019, and in 2020 it was -111%.
The above phenomenon shows that the company’s
D/E ratio is very high, but the resulting profitability
is low. If cash and cash equivalents experience a very
significant decrease and the company’s debt swells,
the risk of default will increase. Suppose the company’s
instability in managing financial conditions continues.
In that case, it will impact the company in a state of
technical insolvency, potentially leading to bankruptcy.
According to Lizal (2002), financial distress can
occur due to the neoclassical, financial, and corporate
governance models.
Predicting the company’s condition can be done
with various models of financial distress analysis.
These models can be used to identify early
symptoms or as a warning before financial distress
or even bankruptcy occurs. At this time, many
financial distress prediction models have been
developed, including the model of Altman (1968),
Grover and Lavin (2001), Springate (1978), Ohlson
(1980), and others. There are several similar studies
on financial distress analysis, including those
conducted by Pratama and Mulyana (2020), and
Masdiantini and Warasniasih (2020) showing differences
in the predictions of the models they use.
1 https://idx.co.id/id/perusahaan-tercatat/laporan-keuangan-dan-tahunan
In addition, studies by Gupita et al. (2020) and
Zebua and Purnomo (2020) show that the most
accurate model is Springate’s model (S-score).
However, as studied by Hastuti (2018), Hungan and
Sawitri (2018), and Indriyanti (2019) argue Grover’s
model (G-score) achieves the most significant level
of accuracy. Studies by Wulandari et al. (2012), and
Salim and Ismudjoko (2021) demonstrate that
Ohlson’s model (O-score) is the most accurate.
Research by Putri and Werastuti (2021), and Masdiantini
and Warasniasih (2020) demonstrates that Fulmer’s
model has the most significant level of accuracy.
Research by Oz and Yelkenci (2015), and Masdiantini
and Warasniasih (2020) demonstrates that Taffler’s
model is the most accurate.
Previous studies showed different research
results. The current study aims to re-examine
the financial distress model in predicting potential
bankruptcy in public and non-public companies in
the building construction sub-sector. The financial
distress prediction models that researchers use are
Springate (1978), Ohlson (1980), Fulmer (1984), Taffler
(1984), and Grover and Lavin (2001). The researchers
also add the latest models developed by Hajdu and
Virág (2001) and Karas and Srbová (2019), which are
specially adapted and applicable to construction
companies. These models can be used in Indonesia,
a member of the Group of Twenty (G20).
As a developing country, Indonesia is actively
building infrastructure in all areas of the economy.
The financial distress prediction model from developed
countries is used for Indonesia by comparing public
and non-public construction companies listed on
the Indonesia Stock Exchange (IDX). During
the observation period of 2014–2020, Indonesia
faces a crisis due to COVID-19, affecting financial
performance.
This study aims to evaluate the financial
instability of construction firms in Indonesia by
categorizing them into two distinct groups: public
and private enterprises. To achieve this, newly
developed models by Hajdu and Virág (2001) and
Karas and Srbová (2019) are utilized, which are
novel in the context of developing countries, in
addition to previously employed models. This study
aims to investigate the factors leading to financial
difficulty and bankruptcy, with a specific focus on
construction enterprises in Indonesia. The objective
is to develop predictive models that can accurately
forecast financial distress and insolvency.
The remainder of the paper is organized as
follows. Section 2 considers the theoretical foundations
of the proposed models and the formulation of
hypotheses. Section 3 describes the research method
and empirical data collected for the study. Section 4
presents the results and discussion of the results.
Finally, Section 5 presents the conclusions of
the study and some recommendations for future
research.
2. LITERATURE REVIEW AND HYPOTHESES
DEVELOPMENT
2.1. Signaling theory
Signaling theory refers to the proactive steps that
management takes to inform investors about
the company’s prospects (Brigham & Houston, 2019).
In addition, Morris (1987, as cited in Palm &
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
137
Bohman, 2023) stated that signaling theory was
developed to deal with the problem of information
asymmetry in the company by providing more
information signals to other parties. According to
this signaling theory, external parties to the company
or users of financial statements outside the company
will determine whether the company’s condition is
positive or negative. Therefore, this study on
financial crisis forecasting analysis will provide
useful information to provide signals to external
parties such as investors, creditors and other users
of financial reports to find out whether the company’s
condition is in good condition or not, so that
investors not make mistakes in investing, and
creditors are not wrong to provide loan funds to
a company.
2.2. Financial distress
Financial distress is the first step that a company
will face before going bankrupt; in these conditions,
the company experiences liquidity difficulties in
paying short-term obligations and company invoices
(Gerritsen, 2015). The condition of financial distress
can be seen in a company’s net income with a negative
value (Aviantara, 2023; Habib et al., 2020; Platt &
Platt, 2002). Financial distress triggers corrective
action by management to improve company
performance (Veganzones & Severin, 2021; Wang
et al., 2021; Whitaker, 1999). The types of financial
distress: 1) economic failure, 2) business failure,
3) technical insolvency, 4) insolvency in bankruptcy,
and 5) legal bankruptcy (Bringham & Gapenski, 1997;
Lipi & Lipi, 2020; Tong & Serrasqueiro, 2021; Voda
et al., 2021). In addition, neoclassical, financial and
corporate management models may be factors that
caused the financial crisis of a company (Lipi & Lipi,
2020; Lizal, 2002; Voda et al., 2021).
2.2.1. Springate’s model
Springate’s model is a financial distress prediction
model, developed in 1978 at Simon Fraser University
by Gordon L. V. Springate. Springate’s model is
a measurement model that uses multiple discriminant
analysis (MDA). The accuracy of this model is 92.5%.
The following equation is (Springate, 1978):
𝑆
_
𝑠𝑐𝑜𝑟𝑒
=
1
.
03
(
𝑌
)
+
3
.
07
(
𝑌
)
+
0
.
66
(
𝑌
)
+
0
.
4
(
𝑌
)
(1)
where,
𝑌 — working capital / total assets;
𝑌 — net profit before interest and taxes / total
assets;
𝑌 — earnings before taxes / current liabilities;
𝑌 — sales / total assets;
Cut-off: 𝑆_𝑠𝑐𝑜𝑟𝑒 > 0.862, non-distress (safe);
𝑆_𝑠𝑐𝑜𝑟𝑒 < 0.862, distress and has the potential for
bankruptcy.
2.2.2. Ohlson’s model
Ohlson (1980) conducted research on financial
distress inspired by previous studies. This model
has a 96.4% level of accuracy in predicting bankruptcy.
The equation for Ohlson’s (1980) model is as follows.
𝑂
_
𝑠𝑐𝑜𝑟𝑒
=
−
1
.
03
−
0
.
407
(
𝑍
)
+
6
.
03
(
𝑍
)
−
1
.
43
(
𝑍
)
+
0
.
0757
(
𝑍
)
−
2
.
37
(
𝑍
)
−
1
.
83
(
𝑍
)
+
0
.
285
(
𝑍
)
−
1
.
72
(
𝑍
)
−
0
.
521
(
𝑍
)
(2)
where,
𝑍 — log (total assets / gross national product
(GNP) price-level index);
𝑍 — total liabilities / total assets;
𝑍 — working capital / total assets;
𝑍 — current liabilities / current assets;
𝑍 — one if total liabilities exceed total assets,
zero otherwise;
𝑍 — net income / total assets;
𝑍 — funds provided by operations / total
liabilities;
𝑍 — one if the net income has been negative
for the past two years, zero otherwise;
𝑍= (𝑁𝑒𝑡− 𝑁𝑒𝑡)/(|𝑁𝑒𝑡
+𝑁𝑒𝑡_𝑖𝑛𝑐𝑜𝑚𝑒|).
Cut-off: 𝑂_𝑠𝑐𝑜𝑟𝑒 < 0.38, non-distress (safe);
𝑂_𝑠𝑐𝑜𝑟𝑒 > 0.38, distress and potentially bankruptcy.
2.2.3. Fulmer’s model
Fulmer’s model was developed in 1984. This model
is one of the prediction models which uses nine
financial ratio variables related to financial distress.
Fulmer’s model has an accuracy rate of 81%–98%.
The following is the equation for Fulmer’s model
(Fulmer et al., 1984).
𝐻
_
𝑠𝑐𝑜𝑟𝑒
=
5
.
528
(
𝑌
)
+
0
.
212
(
𝑌
)
+
0
.
073
(
𝑌
)
+
1
.
27
(
𝑌
)
−
0
.
12
(
𝑌
)
+
2
.
335
(
𝑌
)
+
0
.
575
(
𝑌
)
+
1
.
083
(
𝑌
)
+
0
.
894
(
𝑌
)
−
6
.
075
(3)
where,
𝑌 — retained earnings / total assets;
𝑌 — sales / total assets;
𝑌 — earnings before taxes / total equity;
𝑌 — cash flow from operations / total liabilities;
𝑌 — total liabilities / total assets;
𝑌 — current liabilities / total assets;
𝑌 — logs (fixed assets);
𝑌 — working capital / total liabilities;
𝑌 — log of earnings before interest and taxes
(EBIT) / interest expenses.
Cut-off: 𝐻_𝑠𝑐𝑜𝑟𝑒 > 0, non-distress (safe);
𝐻_𝑠𝑐𝑜𝑟𝑒 < 0, distress and potential for bankruptcy.
2.2.4. Taffler’s model
Taffler’s model was first published by Taffler, R. J.
in 1977. This model was developed with a linear
model and had five ratio indicators. The five
indicators have been improved and modified to
produce four ratio indicators. Taffler uses the MDA
analysis technique with an accuracy rate of 95.7% for
bankruptcy and 100% for non-bankruptcy companies.
The following is the equation for Taffler’s model
(Pech et al., 2020; Taffler, 1984; Weiss et al., 2023).
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
138
𝑇
_
𝑠𝑐𝑜𝑟𝑒
=
0
.
53
(
𝑇
)
+
0
.
13
(
𝑇
)
+
0
.
18
(
𝑇
)
+
0
.
16
(
𝑇
)
(4)
where,
𝑇 — earnings before taxes / current liabilities;
𝑇 — current assets / total liabilities;
𝑇 — current liabilities / total assets;
𝑇 — sales / total assets.
Cut-off: 𝑇_𝑠𝑐𝑜𝑟𝑒 > 0.3, non-distress (safe);
0.2 ≤ 𝑇_𝑠𝑐𝑜𝑟𝑒 ≤ 0.3, gray area; 𝑇_𝑠𝑐𝑜𝑟𝑒 < 0.2, distress
and has the potential for bankruptcy.
2.2.5. Virag and Hajdu’s model
Virág and Hajdu’s (1996) model was a prediction
model developed based on basic accounting for
Hungarian companies from 1990–1991. The research
sample was conducted in 154 companies, of which
77 companies were declared safe and 77 were
declared bankrupt. This model has an accuracy
of 98%. The following is the Hajdu and Virág (2001)
model equation.
𝑉𝐻
_
𝑠𝑐𝑜𝑟𝑒
=
1
.
3566
(
𝑌
)
+
1
.
63397
(
𝑌
)
+
3
.
6638
(
𝑌
)
+
0
.
03366
(
𝑌
)
(5)
where,
𝑌 — cash ratio;
𝑌 — cash flow / total liabilities;
𝑌 — current assets / total assets;
𝑌 — cash flow / total assets.
Cut-off: 𝑉𝐻_𝑠𝑐𝑜𝑟𝑒 > 2.61612, non-distress (safe);
𝑉𝐻_𝑠𝑐𝑜𝑟𝑒 < 2.61612, distress and has the potential
for bankruptcy.
2.2.6. Grover’s model
Jeffrey S. Grover used a sample from the 1968
Altman Z-score model, adding thirteen new financial
ratios and examining the period from 1982 to 1996.
The sample included 70 companies; the results
showed that 35 companies were declared bankrupt,
and 35 other companies were considered safe.
The following is the equation for the Gover model
(Grover & Lavin, 2001).
𝐺
_
𝑠𝑐𝑜𝑟𝑒
=
1
.
650
(
𝑌
)
+
3
.
404
(
𝑌
)
−
0
.
016
(
𝑌
)
+
0
.
057
(
𝑌
)
(6)
where,
𝑌 — working capital/total assets;
𝑌 —EBIT / total assets;
𝑌 — net income / total assets;
𝑌 — cash flow / total assets.
Cut-off: 𝐺_𝑠𝑐𝑜𝑟𝑒 > 0.01, non-distress (safe);
𝐺_𝑠𝑐𝑜𝑟𝑒 < -0.02, distress and potential bankruptcy.
2.2.7. Karas and Srbová’s model
The model by Karas and Srbová (2019) was developed
in the Czech Republic specifically for the construction
industry. The reason for making this model is that
many models are still not compelling enough for use
in construction companies. In their research,
this model produces a high accuracy of 85.71%.
The following is the equation for the model (Karas &
Srbová, 2019; Munir & Bustamam, 2020).
𝑀
_
𝑠𝑐𝑜𝑟𝑒
=
20
.
8
(
𝑌
)
−
12
.
054
(
𝑌
)
+
3
.
116
(
𝑌
)
−
2
.
399
(
𝑌
)
(7)
where,
𝑌 — earnings after taxes (EAT) / total assets;
𝑌 — EBIT / total assets;
𝑌 — retained earnings / total assets;
𝑌 — current liabilities / sales.
Cut-off: 𝑀_𝑠𝑐𝑜𝑟𝑒 < 0.6, non-distress (safe);
𝑀_𝑠𝑐𝑜𝑟𝑒 > -0.6, distress and potential bankruptcy.
2.3. Bankruptcy
Bankruptcy is a condition where a company tend to
experience deficits and company experience liquidation
(Agustia et al., 2020; Gerritsen, 2015; Tron, 2021).
Bankruptcy can be predicted long before the company
goes bankrupt. Hanafi and Halim (2016) explained
that the indicators of bankruptcy are as follows:
1) analysis of cash flow now or for the future;
2) an analysis of the corporate strategies that
focus on the competition faces;
3) cost structure relative to its competitors;
4) quality and management’s capacity to
control costs.
2.4. Hypotheses development
Based on the theoretical background, the hypotheses
of the study are as follows:
H1: There is a significant difference between
the estimated financial distress models in predicting
the bankruptcy of public and non-public companies
in the building construction sub-sector in
the Indonesian capital market.
H2: It is estimated that there is one financial
analysis model that has the highest level of accuracy
in predicting potential bankruptcy in public and non-
public companies in the building construction
sub-sector in the Indonesian capital market.
3. RESEARCH METHODOLOGY
The type of research used is quantitative research
with a descriptive approach. The population in this
study are public and non-public companies in
the building construction sub-sector in the Indonesian
capital market for 2014–2020. The sampling technique
used the purposive sampling method so that eight
sample companies were produced. The object of
the study is IDX-listed companies that have issued
audited financial statements during the observation
period. After selecting the sample, the next step is
determining the category of the company experiencing
financial and non-financial distress. Platt and Platt
(2008) explain the criteria for a sample experiencing
financial distress as follows:
1) public and non-public companies in the building
construction sub-sector which have negative net
profits for two consecutive years;
2) public and non-public companies in the building
construction sub-sector which has not paid
dividends for two consecutive years.
Measurement of financial distress uses Taffler’s,
Fulmer’s, Springate’s, Ohlson’s, Karas and Srbová’s,
Grover’s, and Virág and Hajdu’s models.
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
139
Data normality was tested using the Shapiro-
Wilk test (1965), according to which data are normally
(typically) distributed if the p-value is > 5%; and if
the p-value < 5%, the data is not normally distributed.
Hypothesis testing uses the Kruskal-Wallis test or
H-test, a non-parametric test created by William H.
Kruskal and W. Allen Wallis (Kruskal & Wallis, 1952)
with a Sig. value < 5%. From the test results, if the
Sig. < 5%, then there is a difference and vice versa if
the value is Sig. > 5%, then there is no difference.
Accuracy test and type of error according to Altman
(1968). The accuracy level formula is as follows:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦
=
𝐴𝑐𝑐𝑢𝑟𝑎𝑡𝑒
𝑐𝑜𝑢𝑛𝑡
𝑁
𝑠𝑎𝑚𝑝𝑙𝑒
×
100%
(8)
The type of error is divided into two, namely
type I error (in fact, there is financial distress, but
the results of the prediction show otherwise), and
type II error (in fact, it is non-financial distress, but
the predicted results of the model are experiencing
financial distress). The following is the type of
calculation formula error:
𝑇𝑦𝑝𝑒
𝐼
𝑒𝑟𝑟𝑜𝑟
=
𝑡𝑦𝑝𝑒
𝐼
𝑒𝑟𝑟𝑜𝑟
𝑁
𝑎
×
100%
(9)
𝑇𝑦𝑝𝑒
𝐼𝐼
𝑒𝑟𝑟𝑜𝑟
=
𝑡𝑦𝑝𝑒
𝐼
𝑒𝑟𝑟𝑜𝑟
𝑁
𝑠𝑎𝑚𝑝𝑙𝑒
×
100%
4. RESULTS AND DISCUSSION
Table 1 (Panel A) presents the results of calculations
for public and non-public companies using Springate’s
model, predicting that all companies will file for
bankruptcy. Meanwhile, Ohlson’s, Fulmer’s, Grover’s,
and Karas and Srbová’s models predict all safe
companies. Taffler’s model predicts three safe cases
and one grey area. Virág and Hajdu’s model predicts
three safe companies and one bankruptcy.
Table 1. Model calculation results in public and non-public companies
Model ADHI PTPP WIKA WSKT Model ACST DGIK NRCA SSIA
Panel A Panel B
Springate 0.58792 0.67279 0.61312 0.37377 Springate 0.33238 0.34526 1.26390 0.82353
bankruptcy
bankruptcy
bankruptcy
bankruptcy
bankruptcy
bankruptcy
safe bankruptcy
Ohlson -2.19015 -2.72748 -1.61533 -2.28907 Ohlson 0.32308 -3.13244 -4.02356 -3.89405
safe safe safe safe safe safe safe safe
Fulmer 3.15030 3.45201 1.74732 2.97884 Fulmer -0.58503 1.66676 6.35651 4.15950
safe safe safe safe bankruptcy
safe safe safe
Taffler 0.36440 0.36085 0.35152 0.26564 Taffler 0.35224 0.29527 0.59031 0.36566
safe safe safe gray area safe gray area safe safe
Virág and
Hajdu
3.31827 3.26943 3.07185 2.20875 Virág and
Hajdu
3.16641 2.33664 3.81441 2.46602
safe safe safe bankruptcy
safe bankruptcy
safe bankruptcy
Grover 0.54499 0.61270 0.52177 0.28046 Grover 0.27309 0.27626 0.97893 0.69953
safe safe safe safe safe safe safe safe
Karas and
Srbová
-3.37070 -2.56328 -2.67148 -3.42457 Karas and
Srbová
-4.57786 -2.47802 0.37563 -0.34075
Safe safe safe safe safe safe bankruptcy
bankruptcy
Note: ADHI — PT Adhi Karya Tbk, PTPP — PT PP Tbk, WIKA — PT Wijaya Karya Tbk, WSKT — PT Waskita Karya Tbk, ACST — PT Ascet
Indonusa Tbk, DGIK — PT Nusa Konstruksi Enjiniring Tbk, NRCA — PT Nusa Raya Cipta Tbk, SSIA — PT Surya Semesta Internusa Tbk.
Based on Table 1 (Panel B), the results of
calculations using Springate’s model predict that
one company will be declared safe and three will be
declared bankrupt. Meanwhile, Ohlson’s and Grover’s
models predict safety for all companies. Fulmer’s
model predicts three safe cases and one bankruptcy.
Taffler’s model predicts three safe zones and one
gray zone. Virág and Hajdu’s model predicts two
safeties and two bankruptcies. Karas and Srbová’s
model predicted two safe companies and two
bankruptcies.
Table 2. Descriptive statistics
Model Public companies Count Non-public companies
Min Max Mean SD Min Max Mean SD
Springate -0.351 0.959 0.562 0.256 28 -1.405 1.596 0.691 0.619
Ohlson -3.295 -0.725 -2.206 0.684 28 -4.632 2.611 -2.682 1.972
Fulmer 1.142 3.715 2.832 0.737 28 -3.698 7.188 2.899 2.849
Taffler 0.047 0.448 0.336 0.081 28 0.034 0.754 0.401 0.169
Virág and Hajdu 1.171 3.868 2.967 0.613 28 1.669 4.291 2.946 0.702
Grover -0.327 0.812 0.489 0.228 28 -1.363 1.291 0.557 0.519
Karas and Srbová
-8.587 -1.644 -3.008 1.658 28 -12.23 1.917 -1.755 3.0126
Based on Table 2, each model uses 28 samples,
of which Springate’s model has a minimum value
of -0.351 obtained by WSKT in 2020, so it is predicted
to be the most distressed company and has
the potential to experience bankruptcy. In addition,
PTPP obtained a maximum value of 0.959 in 2014.
This value shows that the company is predicted to
be in a non-distress (healthy) condition. The mean
value of 0.562 illustrates that, on average, all state-
owned companies in the building construction
subsector for the 2014–2020 period are distressed
and have the potential to experience bankruptcy,
while the standard deviation value is 0.256.
The resulting standard deviation value is lower than
the mean value, so the distribution of varying data is
more minor.
From Ohlson’s model, with a minimum value
of -3.295 obtained by PTPP in 2016, it is predicted
to be the most non-distressed (healthy) company.
In addition, WIKA obtained a maximum value
of -0.725 in 2020. This value shows that the company
is predicted to be in a non-distress (healthy)
condition. The mean value of -2.206 illustrates that,
on average, all state-owned companies in the building
construction subsector for the 2014–2020 period are
in a non-distress (healthy) condition, while the standard
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
140
deviation value is 0.684. The resulting standard
deviation value is higher than the mean value, so
the data distribution varies from the mean value.
Fulmer’s model shows a minimum value
of 1.142 obtained by WIKA in 2020, which is
predicted to be the most non-distressed (healthy)
company. The maximum value of 3.715 was obtained
by PTPP in 2016. This value shows that the company
is predicted to be in a non-distress (healthy)
condition. The mean value of 2.832 illustrates that,
on average, all state-owned companies in the building
construction subsector for the 2014–2020 period
are in a non-distress (healthy) condition, while
the standard deviation value is 0.737. The resulting
standard deviation value is lower than the mean
value, so the distribution of varying data is more minor.
Taffler’s model showed a minimum value
of 0.047 for WSKT in 2020, so it is predicted to be
the most distressed company and has the potential
to experience bankruptcy. The maximum value
of 0.448 was obtained by PTPP in 2014, so
the company was predicted to be in a non-distress
(healthy) condition. The mean value of 0.336 illustrates
that, on average, all state-owned companies in
the building construction subsector for the 2014–2020
period are in a non-distress (healthy) condition,
while the standard deviation value is 0.081.
The resulting standard deviation value is lower than
the mean value, so the distribution of varying data is
more minor.
The Virág and Hajda model shows a minimum
value of 1.171 obtained by WSKT in 2020, so it is
predicted to be the most distressed company and
has the potential to experience bankruptcy. ADHI
obtained a maximum value of 3.868 in 2015. This
value shows that the company is predicted to be in
a non-distress (healthy) condition. The mean value
of 2.967 illustrates that, on average, all state-owned
companies in the building construction subsector
for the 2014–2020 period are in a non-distress
(healthy) condition, while the standard deviation
value is 0.613. The resulting standard deviation
value is lower than the mean value, so the distribution
of varying data is more minor.
Meanwhile, Grover’s model shows a minimum
value of -0.327 obtained by WSKT in 2020, is
predicted to be the most distressed company and
has the potential to experience bankruptcy. PTPP
obtained a maximum value of 0.812 in 2014, so
the company is predicted to be in a non-distress
(healthy) condition. The mean value of 0.489 illustrates
that, on average, all state-owned companies in
the building construction subsector for the 2014–2020
period are in a non-distress (healthy) condition,
while the standard deviation value is 0.228.
The resulting standard deviation value is lower than
the mean value, so the distribution of varying data is
more minor.
Based on Table 3, for public companies, only
Ohlson’s model has a p-value > 5%, which is equal
to 0.463. This demonstrates that Ohlson’s model has
normally distributed data. Meanwhile, the remaining
six models had p-values < 5%, namely Fulmer’s
(0.003), Springate’s (0.004), Taffler’s (0.002), Karas
and Srbová’s (0.000), Grover’s (0.002), and Virág and
Hajdu’s (0.031) models. This shows that the six
models have data that are not normally distributed.
Meanwhile, for non-public companies with a degree
of freedom (df) of 28 samples, Fulmer, Taffler, Virág
and Hajdu have p-values > 5%, which is equal to 0.273,
0.898, and 0.760, respectively. This demonstrates
that the three prediction models have normally
distributed data. Meanwhile, the other four models
had p-values <5 %, namely the Springate’s, Ohlson’s,
Grover’s, and Karas and Srbová’s models
of 0.018, 0.000, 0.001, and 0.000, respectively. This
demonstrates that the four models have data that
are not normally distributed. The parametric test
requirements are not fulfilled based on the test
results of the seven prediction models applied to
public and non-public companies. Therefore,
the next test is carried out with a non-parametric
different test (Kruskal-Wallis test).
Table 3. Results of the normality test (Shapiro-Wilk test)
Model Public companies Non-public companies
Stats. df Sig. Stats. df Sig.
Score
Springate 0.881 28 0.004 0.908 28 0.018
Ohlson 0.965 28 0.463 0.790 28 0.000
Fulmer 0.873 28 0.003 0.956 28 0.273
Taffler 0.867 28 0.002 0.982 28 0.898
Virag and Hajda 0.918 28 0.031 0.976 28 0.760
Grover 0.870 28 0.002 0.848 28 0.001
Karas and Srbová 0.716 28 0.000 0.778 28 0.000
Table 4. Kruskal-Wallis test results
Public companies
Non-Public companies
Kruskal Wallis H 175.025 122.830
df 6 6
Asymp. Sig. 0.000 0.000
The test results in Table 4 demonstrate that
public companies have a Kruskal-Wallis-H value
of 175.025, df of six, and Asymp. Sig. equals
0.000 < 0.05. In addition, non-public companies have
a Kruskal-Wallis-H value of 122.830, df of six, and
Asymp. Sig. equals 0.000 < 0.05. Thus, it can be
concluded that H1 is accepted, which means that
there is a significant difference between Fulmer’s,
Springate’s, Ohlson’s, Taffler’s, Karas and Srbová’s,
Grover’s, and Virág and Hajdu’s calculation models
in predicting bankruptcy in public and non-public
companies in the registered building construction
subsector on the IDX for the 2014–2020 period.
Differences in conditions from the results of
the analysis in predicting potential bankruptcy are
caused by the different values, cut-offs, and
financial ratios used in each model.
This study’s results align with Pratama and
Mulyana’s (2020) research which also demonstrates
that the model used can predict financial distress.
Altman predicted 8 distressed, 16 gray areas, and
31 safe; Springate predicted 37 distressed and 18 safe;
Ohlson predicted three distressed and 52 safe; and
Zmijewski predicted 1 distressed. Research by Gupta
et al. (2020) shows differences between the Altman
Z-score, Grover’s, and Springate’s models. Research by
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
141
Zebua and Purnomo (2020) demonstrate that Grover,
Springate, and Zmijewski have significant differences.
According to Hajdu and Virág (2001), there are
differences in bankruptcy prediction using the Altman
model, Springate’s model, Zmijewski’s model, Taffler’s
model, and Fulmer’s model. In addition, the models of
Hajdu and Virág (2001) and Karas and Srbová (2019)
show that the percentage of accuracy differs from
other models, resulting in conditions that are also
different from other models.
Table 5. Calculation of accuracy level and error type in public companies
Real Predictions
Springate Ohlson Fulmer Taffler Virág and Hajdu Grover Karas and Srbová
Distress 0 4 0 0 0 1 0 0
Gray area - - - - 1 0 - -
Non-distressed 4 0 4 4 3 3 4 4
Total 4 4 4 4 4 4 4 4
Level of accuracy 0% 100% 100% 75% 75% 100% 100%
Type I error 0% 0% 0% 0% 0% 0% 0%
Type II error 100% 0% 0% 0% 25% 0% 0%
Gray area - - - 25% - - -
Based on the comparison of the test results by
accuracy level and error type in Table 5, it can be
concluded that Olson’s, Fulmer’s, Grover’s, and
Karas and Srbová’s models are the most accurate
models in predicting the probability of bankruptcy
of public companies in the building construction
sub-sector with a percentage of 100% and type of
error for 0%. They were followed by Taffler’s and
Virág and Hajdu’s models with an accuracy rate
of 75% and a type error of 25%. And finally,
the lowest accuracy rate of 0% and a type of error of
100 owned by the Springate.
Table 6. Calculation of the accuracy level and type of error in non-public companies
Real Predictions
Springate Ohlson Fulmer Taffler Virág and Hajdu Grover Karas and Srbová
Distress 1 3 0 1 0 2 0 2
Gray area - - - - 1 - - -
Non-distressed 3 1 4 3 3 2 4 2
Total 4 4 4 4 4 4 4 4
Level of accuracy 50% 75% 100% 50% 25% 75% 25%
Type I error 0% 25% 0% 25% 25% 25% 25%
Type II error 50% 0% 0% 0% 50% 0% 50%
Gray area - - - 25% - - -
The results of Table 6 for non-public companies
show that only Fulmer’s model has the most
significant accuracy rate of 100% and an error type
of 0%, followed by Ohlson’s and Grover’s models
with an accuracy rate of 75% and a type of error
of 25%. In addition, Springate’s and Taffler’s models
have an accuracy of 50% and an error type of 50%.
Moreover, the models of Virág and Hajdu, and Karas
and Srbová have the lowest accuracy rate of 25% and
a type of error of 75%. This demonstrates that H2 is
accepted, which means that there is a financial
distress analysis model with the most significant
accuracy in predicting potential bankruptcy in public
and non-public companies.
The results of research on public companies
are in line with research conducted by Wulandari
et al. (2012), Oz and Yelkenci (2015), and Salim and
Ismudjoko (2021), which show that Ohlson’s model
is the most accurate. Research by Putri and
Werastuti (2021), and Masdiantini and Warasniasih
(2020) demonstrate that Fulmer’s model has
the most significant level of accuracy. Research by
Hastuti (2018), Hungan and Sawitri (2018), and
Indriyanti (2019) demonstrate that Grover’s model
achieves the most significant level of accuracy.
Research by Karas and Srbová (2019) states that
the model they created is very suitable for use in
construction. However, for non-public companies
the situation is different: only Fulmer’s model has
100% accuracy. This is in line with studies conducted
by Putri and Werastuti (2021), Mustofa and Fahad
Noor (2020), Oz and Yelkenci (2015), Masdiantini
and Warasniasih (2020), which show that Fulmer’s
model is the most accurate.
5. CONCLUSION
The results of this study showed differences in
conditions of the seven models used in predicting
the potential for bankruptcy in public and non-
public companies in the building construction
subsector listed on the IDX for the 2014–2020
period. Public companies show that Ohlson’s,
Fulmer’s, Grover’s, and Karas and Srbová’s models
accurately predict bankruptcy potential. In non-
public companies, only Fulmer’s model has the most
significant accuracy rate of 100% and a type of error
of 0%. The difference in these conditions is caused
by the different cut-offs, values, and financial ratios
used in each model. Companies may get advantages
from this study by considering the use of financial
ratios found in Ohlson’s, Grover’s, Fulmer’s, and
Karas and Srbová’s models as a viable option for
forecasting a company’s situation. In addition, this
study may serve as an anticipatory measure in
the future, enabling internal stakeholders to
enhance corporate performance and implement
necessary enhancements prior to the onset of
financial trouble, which may ultimately result in
bankruptcy. Investors may get advantages from this
study by using Ohlson’s, Grover’s, and Fulmer’s
models as viable alternatives to accurately forecast
the state of a business. This enables investors to
avoid errors when allocating their capital.
Corporate Governance and Organizational Behavior Review / Volume 8, Issue 2, 2024
142
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