Etikonomi Volume 17 (1), 2018: 57 - 68 P-ISSN: 1412-8969;

Received: December 28, 2017; Revised: January 25, 2018; Accepted: February 5, 2018
1, 3 Universitas Malikussaleh. Muara Batu, North Aceh, Aceh, Indonesia
1,2 Universitas Syiah Kuala. Jl. Teuku Nyak Arief, Banda Aceh, Aceh, Indonesia
E-mail:[e-mail],[e-mail],[e-mail]
DOI: htttp://dx.doi.org/10.15408/etk.v17i1.6559
Etikonomi
Volume 17 (1), 2018: 57 - 68
P-ISSN: 1412-8969; E-ISSN: 2461-0771
Ghazali Syamni1,M. Shabri Abd. Majid2, Widyana Verawaty Siregar3
Abstract. Various bankruptcy prediction models have been used to measure the move
of stock prices, and thus the firms’ performance. This study is aimed at empirically exp
the usefulness of the Olhson, Almant Modification, Grover, Springate, and Zmijewski mo
for predicting bankruptcy of the 19 coal mining companies. It also attempts to m
the effects of the scores of these bankruptcy prediction models on the stock prices of th
mining companies in Indonesia. The technique of analysis that used in this research is p
regression. The results of the study showed that the bankruptcy prediction scores of the
and Almant Modification were found to be the dominant prediction models that affected
stock prices of the coal companies in Indonesia. This indicates that the bankruptcy pred
model can be used as one of the approaches to measure the movement of stock prices
performance of the coal mining companies in Indonesia.
Keywords: bankruptcy, stock prices, coal mining companies.
Abstrak. Berbagai model prediksi kebangkrutan telah digunakan untuk mengukur
pergerakan harga saham dan sekaligus kinerja perusahaan. Penelitian ini bertujua
untuk mengeksplorasi secara empiris kegunaan model Olhson, Almant Modificatio
Grover, Springate, dan Zmijewski dalam memprediksi kebangkrutan 19 perusahaa
pertambangan batubara. Penelitian ini juga menguji dampak model prediksi kebangkru
terhadap harga saham perusahaan pertambangan batubara di Indonesia. Teknik analisi
yang dipergunakan pada penelitian ini ialah teknik regresi panel. Hasil penelitian
menemukan bukti bahwa model prediksi Ohlson dan Modifikasi Almant merupaka
model prediksi dominan yang mempengaruhi harga saham perusahaan batubara
Indonesia. Hal ini mengindikasikan bahwa model prediksi kebangkrutan dapat digunaka
untuk memprediksikan pergerakan harga saham dan sekaligus kinerja keuangan indust
batubara di Indonesia.
Kata kunci: kebangkrutan, harga saham, perusahaan pertambangan batubara
How to Cite:
Syamni, G., Majid, M.S.A., & Siregar, W.F. (2018). Bankruptcy Prediction Models and Stock Prices of The Coal Mini
Industry in Indonesia. Etikonomi: Jurnal Ekonomi. Vol. 17 (1): 57 – 68. doi: http//dx.doi.org/10.15408/etk.v17i1.65
Bankruptcy Prediction Models and Stock Prices
of the Coal Mining Industry in Indonesia

Ghazali Syamni. Bankruptcy Prediction Models and Stock Prices
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Introduction
Various approaches have been adopted to measure the company financial perfo
one of them is using the bankruptcy prediction models. The bankruptcy prediction mo
been used to analyze the company performances of different industries. The first ban
prediction model was introduced by Altman (1968), known as Almant Z-Score. This m
has been widely used and still being relevant to predict a company whether it is bank
grey area or healthy (Altman, et al, 2017). In 1995, Edward Almant later modified th
so that it can be used for predicting bankruptcy of manufacturing and non-manufactu
companies.
After 1970s, several other models have been introduced to predict bankruptcy s
as Springate (1978); Ohlson (1980); Zmijewski (1983); and Grover and Lavin (
The names of the models were given based on the name of the researchers who intro
them for the first time. In his study, Jayasekera (2017) have identified four models of
bankruptcy prediction, namely: the mathematic, neural network, statistic and the ma
models. Meanwhile, Wu, et al (2010) categorized the bankruptcy models into the disc
model popularized by Altman in 1968, the logit model introduced by Ohlson in 1980,
probit model developed by Zmijewski in 1984, the hazard model proposed by Shumw
2001, and the Black-Scholes-Merton (BSM) probability model introduced by Hillegeist
al (2004).
In predicting the bankcruptcy, these models have different levels of accur
on their measurements used (Purnajaya and Merkusiwati, 2014). For example, the Oh
model has added the company income variable and in totality, the model has seven v
Meanwhile, the modified Almant and Springate models have the similar four variables
the Olson model, yet they have different types of variables, except the working capit
the total assets. Finally, the models categorized the firm either into the healthy, grey
or bankrupted company with different scores. The detailed measurements of the mod
explained in the methodological section.
Many previous empirical studies in the developed countries have used different
to predict the company performances. For example, in the United States of America;
et al (2013) used the Black-Scholes-Merton (BSM) model to predict the bankruptcy of
non-financial companies. In England, Tinoco and Wilson (2013) predicted the bankrup
by using the network and Almant Z Score models. Ko, et al (2017) predicted the bank
of the solar energy company in Taiwan using the Z score model. Xu and Zhan
predicted the bankruptcy of the listed companies in the Japanese stock exchange usi
Almant Z and the Ohlson scores and then regressed them with the financial performa
the bank institution and Keiretsu firms as the dependent variable.
Similar studies on the bankruptcy prediction in the developing countries h
also used different types of the bankruptcy prediction models. Marcinkevičius a
Kanapickienė (2014) predicted the bankruptcy of the construction company in Lithua
using the Altman, Springate, Taffler and the Tisshaw models. Karas and Režňáková (
measured the bankruptcy prediction of manufacturing company in the Republic of Cz

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Volume 17 (1), 2018: 57 - 68
using the combining models of the discriminant and the Box Cox. In Saudi Arabia, Al-
Kassar and Soileau (2014) predicted the bankruptcy of different companies (i.e., the
transportation, heritage and museum, commercial companies, and the replenishmen
oil companies) using the Z-score model. Similarly, using the Z score model, H
et al (2014) measured bankruptcy of the textiles industry in Pakistan, while Al-Rawi, e
al (2011) investigated the bankruptcy of the glassware company in Jordan. Karamzad
(2013) predicted the bankruptcy of 90 stock exchange companies in Teheran using th
Almant Z score and the Ohlson models. In Thailand, Pongsatat, et al (2004) using the
Almant and Ohlson models to predict the performances of 60 bankrupted companies
60 non-bankrupted companies.
In the context of Indonesia, in predicting the bankruptcy of the companie
previous studies have widely used a single Almant Z Score (Sudiyatno and Pu
2010), while only few other studies used different types of the bankruptcy pre
models for companies of different sectors. For example, Sembiring (2016) used the O
model to predict the bankrupted companies. In predicting the bankruptcy, Rachmaw
(2016) applied the Almant model for the insurance company, Boedi and Tiara
for the telecommunication company, and Yunan and Rahmasari (2015) for Shariah St
Performance.
On the other hand, the previous studies that used more than one models for pre
the bankruptcy of the company in Indonesia. Putera, et al (2017) predicted the bankr
of the mining companies by using the Altman, Springate and Ohlson models. Gunawa
al (2016) applied the Altman, Grover and Zmijewski models for the manufactures com
while Rahayu, et al (2016) used the Altman Z-Score, Springate, and the Zmijewski mo
for the telecommunication companies in Indonesia.
Furthermore, Effendi, et al (2016) used the bankruptcy prediction model o
Springate to estimate the stock prices of the telecommunication companies, while An
and Salean (2016) used the Almant model and analyzed its impact to the pharmacy c
stock prices. Adrian and Khoiruddin (2014) applied the Almant model and anal
impact to the manufacturing company’s stock prices. They documented that th
model affected the stock prices of the manufacturing companies (Adrian and Khoirud
2014), the pharmacy companies (Andriawan and Salean, 2016), and the transp
companies (Amaliawiati and Lestari, 2014) in Indonesia. Effendi et al. (2016) found th
Springate model affected the stock prices, while Wulandari and Norita (2014) found t
Ohlson model (O-score) affected the stock returns of the textile and garment compan
Indonesia over the period 2010-2014. In short, these studies only used a single bankr
prediction model to estimate the stock prices in Indonesia.
The above-reviewed studies showed that many previous empirical studies have
adopted a single model to predict the performance of the firm, and yet its connection
stock prices was rarely done using various bankcruptcy prediction models, thu
insufficient empirical findings. Bankruptcy prediction models might affect the stock p
indicating that when a company goes into bankruptcy, its stock price goes down or u

Ghazali Syamni. Bankruptcy Prediction Models and Stock Prices
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Relying only on a single model to predict the bankcruptcy might lead to an i
estimation, thus it, in turns, lead to the improper policy recommendation. Anticipatin
the present study used various models to predict the bankruptcy of the firms and ana
its impact to the firms’ stock prices in Indonesia. Thus, this study is among the first to
various bankruptcy models of the Olhson, Almant Modification, Grover, Springate, an
Zmijewski to estimate the stock prices in the Indonesian stock markets. This i
novelty of this study that is in its comparison among the bankruptcy prediction mode
Estimating comparatively among these models would show the most suitable and acc
model to be adopted to predict the movement of stock prices in Indonesia. Secondly,
study focuses its analysis on the coal mining companies, which has no previous empi
studies on this important industry in the Indonesian economy. Considering the shortc
and mixed or inconclusive empirical findings of the previous studies on the relationsh
between the bankruptcy prediction models and stock prices in Indonesia, thus this st
hoped to provide a more comprehensive and enriching empirical evidences on this is
comparing various bankruptcy prediction models (i.e., the Olhson, Almant Modificatio
Grover, Springate, and the Zmijewski models) and then analysing its impact to the st
prices of the coal mining industry in Indonesia.
The findings of this study are hoped to shed some lights for the inventor
selecting which companies to invest the monies, for the managers to promote
performance of the companies, and for the regulator to design policy in enhancing th
stock market in the biggest Muslim populous country in the world, Indonesia. The res
of this study is structured as follows. Section 2 provides the empirical framew
followed by the discussion of the findings and their implications in Section 3.
Section 4 concludes the study.
Method
Data of this study is gathered from the financial report of 19 companies in the co
mining sector that are listed on the Indonesian Stock Exchange (IDX) over the
2013 to 2015. These data are accessed through the website www.idx.co.id and firms
selected using the purposive sampling technique. The firms investigated in this study
the coal mining company that published their audited financial reports during the per
2013-2015. As for the stock prices, the closing stock prices of 19 coal-mining compan
are used.
To predict the bankruptcy of the companies, the bankruptcy prediction models o
Olhson, Almant Modification, Grover, Springate, and the Zmijewski are used. The form
description, and score categorization for each model are presented in Table 1.
After measuring the scores for each bankruptcy prediction model, in the next ste
panel regression model is estimated to explore the impacts of bankruptcy prediction
to the stock prices of the coal mining companies in Indonesia. The scores of five bank
prediction models investigated in this study are then treated as the independent vari
predict the stock prices.

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This panel regression model is the most appropriate model to adopt in this study
the study utilizes the aggregated data of time series and cross section data over the
2013 to 2015. The following multiple panel regression model is estimated:
LnSPit=α +β1 OSit +β2 ZMit +β3 GSit +β4 SSit +β5 ZSit +εit
Where LnSP is the natural logarithm of stock prices, OS is the Olson Score, Z
Modified Altman-Z-Score, GS is the Grover Score, SS is the Springate Score,
Zmijewski Score,ε is the error term, i and t is representing firm i for year t.
Table 1. The Bankruptcy Prediction Models
No. Model Formula Description Score Category
1. Springate
(1978)
SS = 1,03X1 + 3,07X2
+0,66X3+0,4X4
SS = Springate Score
X1 = Working capital/Total asset
X2 = Net profit before interest taxes/
total
asset
X3 = Net profit before Taxes/Current
liabilities
X4 = Sales/Total asset
SS > 0,862 = healthy
SS < 0,862= bankrupt
2. Ohlson
(1980)
OS = -1,32-0,407X1 +
6,03X2
– 1,43X3 + 0,0757X4
–2,37X5 – 1,83X6 +
0,285X7
– 1,72X8 – 0,521X9
OS = Ohlon Score
X1 = Log (total assets/GNP index)
X2 = Total liabilities/total assets
X3 = Working capital/total assets
X4 = Current liabilities/current assets
X5 =1 if total liabilities>total assets; 0 if
otherwise
X6 = Net income/total assets
X7 = Cash flow from operations/total
liabilities
X8 = 1 if Net income negative ; 0 if
otherwise
X9 = (NIt – NIt-1) / (NIt + NIt-1)
OS > 0,38 = bankrupt
OS = 0,38 =grey area
OS < 0,38 = healthy
3. Zmijewski
(1983)
Z = -4,3 -4,5X1 + 5,7X2
– 0,004X3
ZS = Zmijewski Score
X1=ROA (Net income/ total assets)
X2= Leverage (Total liabilities/total
assets)
X3 = Liquidity (Current assets/current
liabilities)
ZS > 0 = bankrupt
ZS < 0= health
4. Grover
(2001)
G = 1.650X1 + 3.404X3
–0.016ROA + 0.057
GS = Grover Score
X1 = Working capital/Total assets
X2 = Earnings before interest and
taxes/Total assets
ROA = net income/total assets
GS ≤-0,02 = bankrupt
GS ≥ 0,01 = health
5. Modified
Altman-
Z-Score
(1995)
ZM= 6,56X1 + 3,26X2
+ 6,72X3 + 1,05X4
ZM = Modified Altman-Z-Score
X1 = Working Capital/Total Asset
X2 = Retained Earnings/Total Asset
X3 = Earnings Before Interest and
Taxes/Total Asset
X4 = Book value of (Equity/total debt)
ZM < 1,10 = bankrupt
ZM = 1,10-2,60= grey
ZM > 2,60 = health
In this study, the Chow Test and Hausman test would be firstly conducted in orde
to identify the most appropriate model to be used. The Chow test is conducted to sel

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between the CEM and the FEM, while the Hausman test is conducted to select betwee
FEM and REM. If the result of the p-value of Chow test is insignificant, the CEM is sele
as the most appropriate model. Similarly, if the Hausman test is insignificant, then, th
would select as the most suitable panel regression model, and vice versa. Within the
framework, the model allows to have a different intercept in the regression model am
individual.
Result and Discussion
The findings of the five bankruptcy prediction models of the Olhson, Alma
Modification, Grover, Springate, and the Zmijewski are presented in Table 2. As obser
from the table, of the five models, only the Almant modification model provides answ
the three indicators of 19 coal mining companies. Overall, out of 19 companies, only
companies are predicted to be healthy by five models; they are, Golde Energy Mines
Toba Bara Sejatra Tbk and Indo Tambangraya Megah Tbk. Meanwhile, the Almant mo
could not predict four companies or these companies are categorized in the grey are
companies include Adaro Energy Tbk, Baramulti Suksessarana Tbk, Darma Henw
and Golden Eagle Energy Tbk. In addition, based on the Grover model, 15 companies
predicted in the category of healthy, while the Zmijewski, Almant modification, Spring
and the Olhson models respectively predicted 12, 11, 8, and 3 companies in the cate
healthy.
Furthermore, using the Ohlson model, 3 companies are found to be healthy (3%)
16 companies are found to be bankrupt (16%), and none in the grey area. Th
be concluded that the Ohlson model provides a low prediction because it includes ma
indicators, including the economic growth variable. The Almant modification model fo
companies (11%) to be healthy (H), 4 companies (3%) to be bankrupts (F), and 5 com
(5%) to be in the grey area (G). Grover models documented 15 Healthy companies (1
4 bankrupted companies (4%), and none of the company was in the grey area. The S
found more companies that went bankrupt than the healthy ones, where 10 compani
bankrupt (11%) and 9 healthy companies (8%). The Zmijewski model is the second m
after Grover model that found more healthy companies, that were 12 companies (12%
7 bankrupted companies (7%).
The Grover model found the largest number of companies in the healthy categor
while the Olhson model documented more firms in the bankruptcy category. Finally,
Modified Almant model found more firms in the grey area category. These findings fu
imply that the bankruptcy prediction models have different levels of accuracy in pred
the performance of the firms. Thus, in predicting the performance of the firms, the in
managers, and for policy makers should not only rely on a single bankruptcy model t
the performance of the firm.

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Table 2.
Predicting Bankruptcy Scores of the Coal Mining Companies in Indonesia, 2013-2015
No Company
Score
Pred.
Score
Pred.
Score
Pred.
Score
Pred.
Score
Pred
Olhson Mod.
Altman Grover Springate Zmijewski
1 Adaro Energy Tbk 0.58 F 2.59 G 0.37 H 0.809 F -1.67 H
2 Atlas Resources Tbk 2.72 F -1.57 F -0.88 F -0.638 F -0.17 H
3 Bara Jaya Internasional Tbk 0.75 F 3.07 H 0.56 H 0.733 F -2.28 H
4 Baramulti Suksessarana Tbk 17.87 F 1.19 G 0.12 H 0.669 F 11.40 F
5 Bumi Resources Tbk 5.24 F -9.58 F -2.32 F -1.572 F 4.59 F
6 Bayan Resources Tbk 1.68 F -0.08 F -0.41 H -0.258 F 1.07 F
7 Darma Henwa Tbk 1.65 F 1.67 G 0.27 H 0.119 F -1.54 H
8 Delta Dunia Makmur Tbk 2.64 F 1.00 F 0.42 H 0.466 F 1.43 H
9 Golde Energy Mines Tbk -0.76 H 5.44 H 0.69 H 0.962 H -3.12 H
10 Toba Bara Sejatra Tbk 0.19 H 2.62 H 0.67 H 1.315 H -6.24 H
11 Harum Energy Tbk 2.91 F 10.77 H 0.80 H 1.194 H -3.84 H
12 Indo Tambangraya Megah Tbk-0.06 H 6.10 H 0.97 H 1.764 H -3.10 H
13 Resources Alam Indonesia Tbk3.81 F 8.46 H 0.81 H 1.345 H -9.49 H
14 Mitrabara Adiperdana Tbk 17.49 F 4.45 H 1.15 H 7.938 H 9.20 F
15 Samindo Resource Tbk 13.37 F 4.22 H 0.90 H 1.291 H 1.70 F
16 Perdana Karya Perkasa Tbk 59.34 F 0.45 F -54.62 F -34.12 F -66.71 H
17 Bumi Asam Tbk 0.40 F 6.50 H 1.03 H 1.671 H -2.64 H
18 Petrosea Tbk 1.78 F 2.84 H -2.33 F 0.513 F 0.34 F
19 Golden Eagle Energy Tbk 18.01 F 2.35 G 0.09 H 0.058 F 22.77 F
Healthy 3 (%) 11 (%) 15 (%) 8 (%) 12 (%)
Bankruptcy 16 (%) 3 (%) 4 (%) 11 (%) 7 (%)
Grey Area 0 (%) 5 (%) 0 (%) 0 (%) 0 (%)
Note:
Pred = prediction
H = Health, G = Grey, and F = Bankrupt.
After discussing the findings from the five bankruptcy prediction models, the stu
proceeds to discuss the impact of the bankruptcy prediction scores to the stock price
the coal mining companies in Indonesia. However, as stated earlier, before estimatin
relationship between the bankruptcy prediction scores and the stock prices, we
evaluate first which panel regression model, the CEM, FEM or the REM is the most su
model to estimate the relationship by using the Chow test and Haussmann test. The
found that the p-value of the Chow test is insignificant (p > 0.05), indicating that the
is the most suitable model to estimate. Since the CEM is selected, thus the study did
need to further conduct the Haussmann test to select either the FEM or REM to be us
the best model as the finding from Chow test has confirmed suggesting the CEM as th
suitable model to estimate the impact of the bankruptcy prediction scores to the stoc
of the coal mining companies in Indonesia.

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Table 3. Findings from the Panel Multiple Regression
Variables CEM FEM REM
Constant 6.3345*** 6.3334*** 6.3334***
Ohlson Score -0.0197*** -0.0197*** -0.0197***
Almant-M Score 0.1698*** 0.1698*** 0.1698***
Grover Score -0.0343** -0.0343** -0.0343**
Springate Score -0.0002 -0.0002 -0.0002
Zmijewski Score 0.0317*** 0.0319*** 0.0317***
R2 0.2635 0.2635 26.353
F-Statistic 19.6093*** 11.6296*** 23.6171***
Note: *** and ** indicate significance at the 1% and 5% levels.
As observed from Table 3, the study found that the bankruptcy prediction scores
different models have different effects on the stock prices. However, overall, these pr
scores simultaneously affected the stock prices at the 1% significant level (F-value =
p < 0.01). This finding showed that the bankruptcy prediction score estimated using
models could predict the movements of the stock prices. Thus, the investors,
and policy makers could use the combination of these models at the same time to pr
and stabilize the stock prices of the coal mining companies in Indonesia. This finding
consistent with the earlier studies by Amaliawiati and Lestari (2014), Andriawan and
(2016), and Adrian and Khoiruddin (2014) who documented that the bankruptcy pred
scores significantly affected the stock prices of the telecommunication companies.
However, if each model used to predict the stock price changes partially, the fin
would be different, where not every model has prediction power to estimate the chan
in stock prices in the future. This indicates that to get a better picture on the stock pr
movements, all models need to be considered at the same time when predicting the
prices of the coal mining companies in Indonesia.
As for the findings for the partial relationships, the Springate prediction sc
found to insignificantly affected the stock prices, the findings that contradict Effendi e
(2016) who documented the significant effect of the Springate score on the stock pric
Meanwhile, the Ohlson and Grover prediction scores negatively and significantly affec
stock prices of the mining companies in Indonesia at the 1% and 5% levels of signific
respectively. This indicates that the higher these prediction scores, the stock p
decline, and vice versa.
On the other hand, the Almant modification and Zmijewski prediction scores pos
affected the stock prices at the levels of 1% and 5%, respectively. This implies that th
these prediction scores, the higher would be the stock prices. The positive and signifi
of the Almant prediction score on the stock prices is in harmony with the previous stu
the Indonesian manufacturing companies (Adrian and Khoiruddin (2014), the pharma
companies (Andriawan and Salean (2016), and the transportation companies (Amalia

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and Lestari (2014). Meanwhile, the insignificant effect of the Springate prediction sco
stock prices is in line with the study by Wulandari and Norita (2014).
The different findings across the bankruptcy prediction models were simply due
different measurements used to predict the company’s bankruptcy. These findings fu
imply that the bankruptcy prediction models have different levels of accuracy in pred
the performance of the firms. Thus, in predicting the performance of the firms, the in
managers, and for policy makers should not only rely on a single bankruptcy model t
the performance of the firm.
Our empirical findings on the relationships between the bankruptcy prediction sc
and stock prices offer policy implication to the investors, managers, and regulators. F
investors, they might choose any significant models to analyse the companies’ perfor
but the Almant modification and Ohlson model are found to be the best models used
avoid the loss for investing monies in the coal mining industry in Indonesia. A
regulators, particularly the Financial Service Authority of Indonesia should rely
bankruptcy prediction scores to control the performances of the stock prices o
mining companies, as part of ensuring the stability of the national stock market. Simi
manager of the companies might also promote the performances of the firms by refe
the bankruptcy prediction score as the sources of policy references.
Conclusion
The bankruptcy prediction models offer different findings of the performances of
coal mining companies in Indonesia over the period 2013-2015. The Grover model fo
largest number of companies in the healthy category, while the Olhson model docum
more firms in the bankruptcy category. Meanwhile, the Modified Almant model found
firms in the grey area category. As for the relationships between the bankruptcy pred
scores and stock prices, the study found that the Springate prediction score was insig
affected the stock prices. Meanwhile, the Ohlson and Grover prediction scores are fou
affect negatively and significantly the stock prices of the mining companies in Indone
indicating that the higher these prediction scores, the lower would be the stock price
vice versa. On the other hand, the Almant modification and Zmijewski prediction scor
found to positively affect the stock prices, implying the higher these prediction score
higher would be the stock prices.
These findings further suggest that the investors should give more attenti
Ohlson and Grover models, because they gave negative prediction towards the stock
This means that when these models’ prediction value is at one point in a company, th
would be a stock price reduction at one point in the future. As for the regulators, part
the Financial Service Authority of Indonesia, it is suggested to rely on the ban
prediction scores to control the performances of the stock prices, and in turns, the st
of the national stock market. Finally, the manager of the companies might also prom
performances of the firms by referring to the bankruptcy prediction score as the sour
policy references. To provide more comprehensive findings, future studies on this iss

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suggested to consider more companies across the industries to be used as the sampl
study since different sector of industries has different characteristics. Additiona
studies are also suggested to use a longer period of data so that it could provide a cle
picture of the bankruptcy prediction-stock prices relations.
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