Predicting Financial Distress in Indonesian Manufacturing Industry
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Data Science and Service Research
Discussion Paper
Discussion Paper No. 62
Predicting Financial Distress in Indonesian
Manufacturing Industry
MUHAMMAD RIFQI and YOSHIO KANAZAKI
June, 2016
May 2016
Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University
27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPAN
Discussion Paper
Discussion Paper No. 62
Predicting Financial Distress in Indonesian
Manufacturing Industry
MUHAMMAD RIFQI and YOSHIO KANAZAKI
June, 2016
May 2016
Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University
27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPAN
1
Predicting Financial Distress in Indonesian Manufacturing
Industry
MUHAMMAD RIFQI and YOSHIO KANAZAKI*
ABSTRACT
We attempt to develop and evaluate financial distress prediction models using
financial ratios derived from financial statements of companies in Indonesian
manufacturing industry. The samples are manufacturing companies listed in
Indonesian Stock Exchange during 2003-2011. The models employ two kinds of
methods: traditional statistical modeling (Logistic Regression and Discriminant
Analysis) and modern modeling tool (Neural Network). We evaluate 23 financial
ratios (that measure a company’s liquidity, profitability, leverage, and cash
position) and are able to identify a set of ratios that significantly contribute to
financial distress condition of the companies in sample group. By utilizing those
ratios, prediction models are developed and evaluated based on accuracy and
error rates to determine the best model. The result shows that the ratios
identified by logistic regression and the model built on that basis is more
appropriate than those derived from discriminant analysis. The research also
shows that although the best performing prediction model is a neural network
model, but we have no solid proof of neural network’s absolute superiority over
traditional modeling methods.
Keywords: financial distress, prediction model, discriminant analysis, logistic
regression, neural network.
1. INTRODUCTION
The topic of financial distress prediction has been attracting many researchers’ attention,
especially those in accounting field. Financial distress prediction models have been created
to cope with financial difficulties condition faced by companies, especially in post-crisis
period (Shirata, 1998). The development of prediction models started when Beaver
introduced a simple univariate analysis of financial ratios to predict future bankruptcy
(Beaver, 1966). Since then, many researchers have been struggling to develop financial
distress prediction techniques using statistical models. The most popular example was
* Graduate School of Economics and Management, Tohoku University, Sendai, Japan
This work was supported by JSPS KAKENHI Grant Number JP25380385.
Predicting Financial Distress in Indonesian Manufacturing
Industry
MUHAMMAD RIFQI and YOSHIO KANAZAKI*
ABSTRACT
We attempt to develop and evaluate financial distress prediction models using
financial ratios derived from financial statements of companies in Indonesian
manufacturing industry. The samples are manufacturing companies listed in
Indonesian Stock Exchange during 2003-2011. The models employ two kinds of
methods: traditional statistical modeling (Logistic Regression and Discriminant
Analysis) and modern modeling tool (Neural Network). We evaluate 23 financial
ratios (that measure a company’s liquidity, profitability, leverage, and cash
position) and are able to identify a set of ratios that significantly contribute to
financial distress condition of the companies in sample group. By utilizing those
ratios, prediction models are developed and evaluated based on accuracy and
error rates to determine the best model. The result shows that the ratios
identified by logistic regression and the model built on that basis is more
appropriate than those derived from discriminant analysis. The research also
shows that although the best performing prediction model is a neural network
model, but we have no solid proof of neural network’s absolute superiority over
traditional modeling methods.
Keywords: financial distress, prediction model, discriminant analysis, logistic
regression, neural network.
1. INTRODUCTION
The topic of financial distress prediction has been attracting many researchers’ attention,
especially those in accounting field. Financial distress prediction models have been created
to cope with financial difficulties condition faced by companies, especially in post-crisis
period (Shirata, 1998). The development of prediction models started when Beaver
introduced a simple univariate analysis of financial ratios to predict future bankruptcy
(Beaver, 1966). Since then, many researchers have been struggling to develop financial
distress prediction techniques using statistical models. The most popular example was
* Graduate School of Economics and Management, Tohoku University, Sendai, Japan
This work was supported by JSPS KAKENHI Grant Number JP25380385.
2
Altman Z-Score model which utilizes 5 different financial ratios in his prediction model
(Altman, 1968). Other notable models include Ohlson model in 1980 (Ohlson, 1980), Fulmer
model in 1984 (Fulmer, 1984), and Springate model in 1978 (Springate, 1978). Besides
western researchers, accounting researchers from Asia also present their models, such as
Shirata who presented her first model in 1998 and then updating it in 2003 (the updated
version, being known as SAF2002 model, is widely used in Japan). Sung, Chang, and Lee
(1999) analyzes financial pattern and significant financial ratios to discriminate future
bankrupt companies under different macroeconomic circumstances. Bae (2012) develops a
distress prediction model based on radial basis support vector machine (RSVM) for
companies in South Korean manufacturing industry. In the case of Indonesia, there have
been several but still limited models developed by researchers to predict financial distress.
Indonesian researchers focused mainly on Indonesian manufacturing Industry, such as
Luciana (2003) and Brahmana (2005).
It is important to note that “financial distress” and “bankruptcy” is not the same thing.
Financial distress typically takes place before bankruptcy; therefore it can be considered as
an indicator of bankruptcy (Luciana, 2003). Due to the convenience in obtaining the legal
data and the relatively efficient process of bankruptcy filing, most researchers that use US
companies in their study use the legal definition of bankruptcy in their prediction models. In
other words, they classify the firms which filed for bankruptcy in legal court as the
“bankrupt” group, thus they are developing bankruptcy prediction models, not financial
distress prediction models. Same thing also applies in relatively developed countries where
the bankruptcy filing process can be conducted efficiently, such as Canada (Springate, 1978)
and Japan (Shirata, 1998). Meanwhile, some other researchers use delisting status from the
exchange as their bankruptcy proxy, for example Shumway (2001).
However, for researchers who take the companies in developing economies as their
sample, using legal definition of bankruptcy might pose a grave problem. This is due to the
fact that bankruptcy filing process in a developing country typically takes years to complete,
so it will be a long process until a company can be declared bankrupt. For example, in the
case of Indonesia, a bankruptcy filing process in court usually takes a considerably long time
to undergo, and the data of bankruptcy filing is very hard to obtain from Indonesian
Corporate Court (Zu’amah, 2005). If they decided to use the bankruptcy data for their
prediction models, there will be a significant amount of time lag between the date of
bankruptcy declaration and the financial numbers they use to predict the bankruptcy event,
thus greatly reducing the relevance of their model to predicting the bankruptcy event. Due
to this problem, the researchers in developing countries resort to an alternative strategy:
they use “financial distress” status instead of “bankruptcy” status, thus making their
Altman Z-Score model which utilizes 5 different financial ratios in his prediction model
(Altman, 1968). Other notable models include Ohlson model in 1980 (Ohlson, 1980), Fulmer
model in 1984 (Fulmer, 1984), and Springate model in 1978 (Springate, 1978). Besides
western researchers, accounting researchers from Asia also present their models, such as
Shirata who presented her first model in 1998 and then updating it in 2003 (the updated
version, being known as SAF2002 model, is widely used in Japan). Sung, Chang, and Lee
(1999) analyzes financial pattern and significant financial ratios to discriminate future
bankrupt companies under different macroeconomic circumstances. Bae (2012) develops a
distress prediction model based on radial basis support vector machine (RSVM) for
companies in South Korean manufacturing industry. In the case of Indonesia, there have
been several but still limited models developed by researchers to predict financial distress.
Indonesian researchers focused mainly on Indonesian manufacturing Industry, such as
Luciana (2003) and Brahmana (2005).
It is important to note that “financial distress” and “bankruptcy” is not the same thing.
Financial distress typically takes place before bankruptcy; therefore it can be considered as
an indicator of bankruptcy (Luciana, 2003). Due to the convenience in obtaining the legal
data and the relatively efficient process of bankruptcy filing, most researchers that use US
companies in their study use the legal definition of bankruptcy in their prediction models. In
other words, they classify the firms which filed for bankruptcy in legal court as the
“bankrupt” group, thus they are developing bankruptcy prediction models, not financial
distress prediction models. Same thing also applies in relatively developed countries where
the bankruptcy filing process can be conducted efficiently, such as Canada (Springate, 1978)
and Japan (Shirata, 1998). Meanwhile, some other researchers use delisting status from the
exchange as their bankruptcy proxy, for example Shumway (2001).
However, for researchers who take the companies in developing economies as their
sample, using legal definition of bankruptcy might pose a grave problem. This is due to the
fact that bankruptcy filing process in a developing country typically takes years to complete,
so it will be a long process until a company can be declared bankrupt. For example, in the
case of Indonesia, a bankruptcy filing process in court usually takes a considerably long time
to undergo, and the data of bankruptcy filing is very hard to obtain from Indonesian
Corporate Court (Zu’amah, 2005). If they decided to use the bankruptcy data for their
prediction models, there will be a significant amount of time lag between the date of
bankruptcy declaration and the financial numbers they use to predict the bankruptcy event,
thus greatly reducing the relevance of their model to predicting the bankruptcy event. Due
to this problem, the researchers in developing countries resort to an alternative strategy:
they use “financial distress” status instead of “bankruptcy” status, thus making their
3
prediction models a little different in nature to those of developed countries. However, in
this study we will use the term “financial distress” and “bankruptcy” interchangeably.
It is necessary to understand that there is no single accurate definition of the term
“financial distress” itself. Hofer (1980) as noted in Luciana (2006) defines “financial distress”
as a condition in which a company suffers from negative net income for a consecutive period.
Luciana (2006) herself defines “financial distress” as a condition in which a company is
delisted as a consequence of having negative net income and negative equity. Whitaker
(1999) identifies the condition in which the cash flow of a company is less than the current
portion of company’s long-term debt as definition of company in “financial distress”. Keasey,
et. Al. (2009) and Asquith, Gertner, and Scharfstein (1994) classify a firm as “financially
distressed” if the company’s EBITDA is less than its financial expense for two consecutive
years. Lau (1987) prefers to see “financial distress” as a condition in which a company omits
or reduces dividend payment to its shareholders. In our study, we decided to use the
financial distress definition as stated by Ross (2008) and Luciana (2006), i.e. the book value
of total debt exceeding the book value of total asset.
The statistical methods used to analyze the variables and constructing the model also
vary between researchers. Early researchers in this field used discriminant analysis in their
studies. Beaver (1966) used univariate form of discriminant analysis in his paper, while
multivariate discriminate analysis was used by Altman (1968) in his Z-score model and
Springate (1984). Then Ohlson (1980) opened the alternative way by utilizing logistic
regression in bankruptcy prediction models. Zmijewski (1983) followed suit by also applying
logistic regression analysis in his model.
Revolutionary development of computer science in 1980s also gave rise to several
alternative methods of data analysis researchers can use in constructing prediction models.
Among those methods is neural network. The earliest financial distress study that utilized
neural network method was a study by Odom and Sharda (1990). Several notable researches
that used neural network include Tam and Kiang (1992), Zhang, et. Al. (1999), Atiya (2001),
Virag and Kristof (2005), and Rafiei, et. Al. (2011).
The remainder of the paper is organized as follows. Section 2 describes the data and sample
used in the study. Section 3 discusses the evaluation and selection of best variables to be
included in the model. Section 4 attempts to construct prediction models and analyze them
based on accuracy and error rate. Section 5 concludes the paper and discusses possible
future research ideas.
prediction models a little different in nature to those of developed countries. However, in
this study we will use the term “financial distress” and “bankruptcy” interchangeably.
It is necessary to understand that there is no single accurate definition of the term
“financial distress” itself. Hofer (1980) as noted in Luciana (2006) defines “financial distress”
as a condition in which a company suffers from negative net income for a consecutive period.
Luciana (2006) herself defines “financial distress” as a condition in which a company is
delisted as a consequence of having negative net income and negative equity. Whitaker
(1999) identifies the condition in which the cash flow of a company is less than the current
portion of company’s long-term debt as definition of company in “financial distress”. Keasey,
et. Al. (2009) and Asquith, Gertner, and Scharfstein (1994) classify a firm as “financially
distressed” if the company’s EBITDA is less than its financial expense for two consecutive
years. Lau (1987) prefers to see “financial distress” as a condition in which a company omits
or reduces dividend payment to its shareholders. In our study, we decided to use the
financial distress definition as stated by Ross (2008) and Luciana (2006), i.e. the book value
of total debt exceeding the book value of total asset.
The statistical methods used to analyze the variables and constructing the model also
vary between researchers. Early researchers in this field used discriminant analysis in their
studies. Beaver (1966) used univariate form of discriminant analysis in his paper, while
multivariate discriminate analysis was used by Altman (1968) in his Z-score model and
Springate (1984). Then Ohlson (1980) opened the alternative way by utilizing logistic
regression in bankruptcy prediction models. Zmijewski (1983) followed suit by also applying
logistic regression analysis in his model.
Revolutionary development of computer science in 1980s also gave rise to several
alternative methods of data analysis researchers can use in constructing prediction models.
Among those methods is neural network. The earliest financial distress study that utilized
neural network method was a study by Odom and Sharda (1990). Several notable researches
that used neural network include Tam and Kiang (1992), Zhang, et. Al. (1999), Atiya (2001),
Virag and Kristof (2005), and Rafiei, et. Al. (2011).
The remainder of the paper is organized as follows. Section 2 describes the data and sample
used in the study. Section 3 discusses the evaluation and selection of best variables to be
included in the model. Section 4 attempts to construct prediction models and analyze them
based on accuracy and error rate. Section 5 concludes the paper and discusses possible
future research ideas.
absas
2/17/2019, 8:21:20 AMabsas
2/17/2019, 8:21:14 AM
4
2. DATA AND SAMPLE
Total sample for our study is 147 companies in Indonesian manufacturing industry over
the course of 9 years (2003-2011). Such time period is chosen due to the availability of data,
and also accounting for post-crisis recovery period. Also included in the sample are the
companies that were delisted from Indonesian Stock Exchange (IDX) and the companies
that changed their core industry either from or to manufacturing industry. We obtain the
data from 2 sources: OSIRIS database of Indonesian public companies and audited financial
statements publicly available from from IDX website (www.idx.co.id).
Among those 147 companies, we notice after analyzing the descriptive statistics that one
company is an outlier (MYRX 2009). In order to avoid misrepresentation and unreliable
model results, we decide to exclude the outlier from our sample. Moreover, we also exclude
11 companies with incomplete financial data. We also prepare a set of holdout sample to be
used as validation measures, in which we calculate the accuracy and error rates of resulting
models to see whether they perform well in the companies not included in the making of the
models.
We examine as many as 23 ratios from each sample’s financial statements. We derive
and compile these 23 ratios from previous prediction models, including Altman (1968),
Ohlson (1980), Zmijewski (1983), Springate (1984), Fulmer (1984), Shirata (1998),
Brahmana (2005), and Luciana (2006). Full list of the ratios description is available in
Appendix I. Table 1 displays the descriptive statistics of training sample, split between
distress and non-distress sub-groups.
From the table, we are able to imply that most of the ratios are in-line with our logical
expectation. In overall, non-distress firms have substantially lower average debt level than
distress ones, either in terms of current liability, long-term debt, or total liability. However,
we notice an unexpected anomaly between distress and non-distress in terms of earnings.
The descriptive statistics indicates that distress firms have higher earnings in average than
non-distress firms. The distress sub-group posted higher NITA ( 0.154799), EBTEQ ( 0.166126 ),
LOGEBITINT ( 0.187298 ), and GRONITA ( -0.26612 ) than non-distress one ( 0.037932, -0.78836,
-0.32594 , and -1.02886 respectively). Higher level of debt and higher earnings exhibited by
distress firms could indicate a tendency distress firms taking higher risk in its balance sheet
by intensively using financial leverage in order to achieve higher earnings.
Moreover, we could also notice from the table that distress firms have higher FATA in
average. This indicates that distress firms not only increase their risk on financial but also
on operating leverage front, by employing higher long-term investments which are usually
financed by debts.
2. DATA AND SAMPLE
Total sample for our study is 147 companies in Indonesian manufacturing industry over
the course of 9 years (2003-2011). Such time period is chosen due to the availability of data,
and also accounting for post-crisis recovery period. Also included in the sample are the
companies that were delisted from Indonesian Stock Exchange (IDX) and the companies
that changed their core industry either from or to manufacturing industry. We obtain the
data from 2 sources: OSIRIS database of Indonesian public companies and audited financial
statements publicly available from from IDX website (www.idx.co.id).
Among those 147 companies, we notice after analyzing the descriptive statistics that one
company is an outlier (MYRX 2009). In order to avoid misrepresentation and unreliable
model results, we decide to exclude the outlier from our sample. Moreover, we also exclude
11 companies with incomplete financial data. We also prepare a set of holdout sample to be
used as validation measures, in which we calculate the accuracy and error rates of resulting
models to see whether they perform well in the companies not included in the making of the
models.
We examine as many as 23 ratios from each sample’s financial statements. We derive
and compile these 23 ratios from previous prediction models, including Altman (1968),
Ohlson (1980), Zmijewski (1983), Springate (1984), Fulmer (1984), Shirata (1998),
Brahmana (2005), and Luciana (2006). Full list of the ratios description is available in
Appendix I. Table 1 displays the descriptive statistics of training sample, split between
distress and non-distress sub-groups.
From the table, we are able to imply that most of the ratios are in-line with our logical
expectation. In overall, non-distress firms have substantially lower average debt level than
distress ones, either in terms of current liability, long-term debt, or total liability. However,
we notice an unexpected anomaly between distress and non-distress in terms of earnings.
The descriptive statistics indicates that distress firms have higher earnings in average than
non-distress firms. The distress sub-group posted higher NITA ( 0.154799), EBTEQ ( 0.166126 ),
LOGEBITINT ( 0.187298 ), and GRONITA ( -0.26612 ) than non-distress one ( 0.037932, -0.78836,
-0.32594 , and -1.02886 respectively). Higher level of debt and higher earnings exhibited by
distress firms could indicate a tendency distress firms taking higher risk in its balance sheet
by intensively using financial leverage in order to achieve higher earnings.
Moreover, we could also notice from the table that distress firms have higher FATA in
average. This indicates that distress firms not only increase their risk on financial but also
on operating leverage front, by employing higher long-term investments which are usually
financed by debts.
5
Full Sample Distress Non-Distress
Ratios Average St. Dev Average St. Dev Average St. Dev
WCTA 0.022355 0.53551 -0.93178 0.813432 0.169145 0.259789
RETA -0.31367 1.069294 -2.1729 1.453425 -0.02763 0.616991
EBITTA 0.061706 0.136507 -0.08166 0.16612 0.083762 0.116649
MVEBVTL 1.905372 3.979102 0.279917 0.311263 2.155442 4.217252
STA 1.117411 0.842006 1.003946 0.898432 1.134867 0.831613
NPBTCL 0.295269 0.942091 -0.07611 0.612256 0.352403 0.970529
TLTA 0.750742 0.704036 2.180809 0.997199 0.530731 0.236439
CLCA 1.605552 3.771468 5.476566 6.444037 1.010011 2.713615
NITA 0.053514 0.417473 0.154799 1.113171 0.037932 0.092937
CFOTL 0.095313 0.522357 -0.04353 0.140184 0.116674 0.555328
CACL 2.241208 3.471047 1.097209 2.926721 2.417208 3.514486
EBTEQ -0.26033 5.18975 0.166126 0.539016 -0.32594 5.567777
CLTA 0.457709 0.484578 1.302884 0.827566 0.327683 0.196914
LOGTGTA 8.667008 0.986768 8.272618 0.903239 8.727684 0.985083
WCTD 42.1321 235.5618 -0.57969 0.458834 48.70314 252.3932
LOGEBITINT -0.8667 1.988883 0.187298 1.381826 -1.02886 2.01823
GROTLEQ 0.240846 1.717569 -0.03132 0.297076 0.282718 1.837708
INTDISEXPSTB -0.54851 4.72851 -0.03529 0.0512 -0.62747 5.07459
AP12S 2.670078 6.675749 8.416431 16.52646 1.786024 1.881802
NIS 0.015204 0.936922 -0.03725 2.54886 0.023275 0.113547
GRONITA -0.71873 5.934713 -0.26612 2.402211 -0.78836 6.302004
FATA 0.51945 0.214259 0.625252 0.229852 0.503173 0.207013
LNTA 19.95652 2.272117 19.04841 2.079785 20.09623 2.268236
Table 1 Descriptive Statistics
3. VARIABLE SELECTION
Working from the full set of 23 ratios, we perform the procedure to carefully evaluate the
ratios and to eventually choose a set of ratios that will make the best models. In order to do
this, we use two different procedures, namely stepwise logit and stepwise discriminant
analysis procedures. The outcome of these procedures is two set of “best” ratios.
Full Sample Distress Non-Distress
Ratios Average St. Dev Average St. Dev Average St. Dev
WCTA 0.022355 0.53551 -0.93178 0.813432 0.169145 0.259789
RETA -0.31367 1.069294 -2.1729 1.453425 -0.02763 0.616991
EBITTA 0.061706 0.136507 -0.08166 0.16612 0.083762 0.116649
MVEBVTL 1.905372 3.979102 0.279917 0.311263 2.155442 4.217252
STA 1.117411 0.842006 1.003946 0.898432 1.134867 0.831613
NPBTCL 0.295269 0.942091 -0.07611 0.612256 0.352403 0.970529
TLTA 0.750742 0.704036 2.180809 0.997199 0.530731 0.236439
CLCA 1.605552 3.771468 5.476566 6.444037 1.010011 2.713615
NITA 0.053514 0.417473 0.154799 1.113171 0.037932 0.092937
CFOTL 0.095313 0.522357 -0.04353 0.140184 0.116674 0.555328
CACL 2.241208 3.471047 1.097209 2.926721 2.417208 3.514486
EBTEQ -0.26033 5.18975 0.166126 0.539016 -0.32594 5.567777
CLTA 0.457709 0.484578 1.302884 0.827566 0.327683 0.196914
LOGTGTA 8.667008 0.986768 8.272618 0.903239 8.727684 0.985083
WCTD 42.1321 235.5618 -0.57969 0.458834 48.70314 252.3932
LOGEBITINT -0.8667 1.988883 0.187298 1.381826 -1.02886 2.01823
GROTLEQ 0.240846 1.717569 -0.03132 0.297076 0.282718 1.837708
INTDISEXPSTB -0.54851 4.72851 -0.03529 0.0512 -0.62747 5.07459
AP12S 2.670078 6.675749 8.416431 16.52646 1.786024 1.881802
NIS 0.015204 0.936922 -0.03725 2.54886 0.023275 0.113547
GRONITA -0.71873 5.934713 -0.26612 2.402211 -0.78836 6.302004
FATA 0.51945 0.214259 0.625252 0.229852 0.503173 0.207013
LNTA 19.95652 2.272117 19.04841 2.079785 20.09623 2.268236
Table 1 Descriptive Statistics
3. VARIABLE SELECTION
Working from the full set of 23 ratios, we perform the procedure to carefully evaluate the
ratios and to eventually choose a set of ratios that will make the best models. In order to do
this, we use two different procedures, namely stepwise logit and stepwise discriminant
analysis procedures. The outcome of these procedures is two set of “best” ratios.
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