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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2020; 1(4): | Volume:1 Issue:4 ( Nov. 25, 2020 ) : 362-371.
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DOI : 10.47310/iarjbm.2020.v01i04.021       Download PDF       HTML       XML

Comparative Analysis of Models Altman and Zmijewski Financial Distress Prediction in the Indonesian Sharia Banking Sector


Article History

Received: 25.10.2020; Revision: 04. 11.2020; Accepted: 19. 11.2020; Published: 25. 11.2020

Author Details

Surdiyono1, Suharto1 and Hary Indratjahyo1

Authors Affiliations

1Universitas Krisnadwipayana Campus Unkris Jatiwaringin PO BOX 7774/Jat CM Jakarta 13077, Indonesia


Abstract: This study aims at financial distress in Islamic Commercial Banks with the Altman model (Z-Score), predicting financial distress in Islamic Commercial Banks in Indonesia with the Zmijewski model (X-Score) and analyzing the comparison to predict financial distress in the Islamic banking sector in Indonesia using the Z-Score and X-Score models. The data used in this analysis are Islamic bank companies in 2018. The data were analyzed using the Z-Score and X-Score models. Based on data analysis using the Altman Z-Score analysis model, it is known that the number of Islamic banks in the gray zone is 2 or 16.67%, while in the distress zone, there are ten banks or 83.33%. The results of the X-Score analysis by Zmijewski's model showed that three companies did not experience distress or as much as 25.0%, and the remaining nine companies experienced distress or as much as 75.0%. The comparative analysis result shows that five banks have different analysis results or 41.67%, while 58.33% have the same analysis results. This difference is significant, so it can be said that the results of the bankruptcy analysis based on Altman's Z-Score and Zmijweski's X-Score differ significantly.


Keywords: financial distress, Altman's Z-Score method, and Zmijewski's X-Score.


Introduction

The economy of Indonesia and the world grows and develops with various kinds of financial institutions, one of which is a bank. The bank is a significant financial institution, namely as a financial intermediary in which the role of the bank is to become a medium for collecting funds from people who have excess funds (surplus parties) and channel them back to those in need (deficit). The development of banks was so rapid, but the 1998 crisis ultimately had a lot of impact on economic growth both in the real sector and in the banking sector, which was the coffers that flowed funds to all sectors of the economy, costing a lot of restructuring, namely 75% of Indonesia's GDP (Kuncoro, 2001).


This is because the banking sector plays a crucial role in the economy in Indonesia. As an intermediary institution, banking is directly related to the development of the whole industry and money circulation in society. During the 1998 monetary crisis, in a period of two years, namely 1997-1999, at least 64 banks were experiencing financial problems so that regulators had to take several actions such as liquidation, freezing of business activities, terminating operations, expropriation, and recapitalization (Shidiq, 2013); (Wibowo, 2013). The 1998 crisis reduced public confidence in banks so that the government had to act to save the banking sector and restore public trust. Banks also have to look for factors that have the potential to cause financial distress.


Sharia banking is a banking system developed based on the Islamic system (Islamic law). The effort to form this system departs from the Islamic prohibition of collecting and borrowing based on the interest included in usury and investments for businesses categorized as haram, for example, in food, beverages, and other non-Islamic companies, which are not regulated in Conventional Banks.


Sharia banking, as a sharia financial institution, initially developed slowly but then began to show progressively faster progress, achieving growth compared to conventional banking. In Indonesia, Islamic banking has emerged since the issuance of Law No.7 of 1992 concerning Banking, which was subsequently updated in Law No.10 of 1998, which implicitly opens opportunities for banking business activities that have an operational basis profit sharing. The existence of Islamic banks, both operating stand-alone and as functional units of conventional banks, is an effort to meet the community's very diverse needs (Antonio, 2001). Sharia banking in Indonesia, first operated on May 1, 1992, marked Bank Muamalat Indonesia (BMI).Islamic banks are financial institutions whose primary business is to provide financing and other services in payment traffic and circulation of money whose operations follow Islamic religious principles (Sudarsono, 2004). One of these Islamic banks is characterized by a profit-sharing system (non-interest) for profit sharing. The amount of this profit sharing is determined at the beginning of the agreement. In contrast to interest, the percentage of this profit sharing is not necessarily the same every month.


The profit-sharing principle is a general characteristic and the necessary foundation for Islamic banks' operation as a whole. In sharia, this principle is based on the code of al mudharaba. Based on this principle, Islamic banks will function as partners, both with savers and entrepreneurs who borrow funds. Banks will act as mudharib (fund managers) with savers, while savers act as shahibul maal (fund owners). A mudharabah agreement was held between the two, which stated the profit-sharing of each party (Antonio, 2001). As in conventional banking, Islamic banking also depends on depositors who keep their money in the bank. Along with the increase in public knowledge about Islamic banking, the profit-sharing rate becomes an incentive for depositors to save their money in Islamic banks.


The characteristics of the sharia banking system that operates based on profit sharing principles provide an alternative banking system that is mutually beneficial for the public and the bank, as well as highlighting aspects of fairness in transactions, ethical investment, prioritizing the values ​​of togetherness and brotherhood in production, and avoiding speculative activities in financial transactions. By providing various banking products and services with more varied financial schemes, Islamic banking has become an alternative to a credible banking system that all Indonesian people can enjoy without exception.


This strength is indeed inseparable from the establishment of various Islamic banking institutions in Indonesia, including Sharia Commercial Banks, Sharia Business Units, and Sharia Rural Banks. Based on statistical data on the development of Sharia Banking in 2018, there were 14 Islamic Commercial Banks (BUS), 20 Sharia Business Units (UUS), and 167 Sharia Rural Banks (BPRS).

Figure image is available in PDF Format

Source: OJK, December 2018 (Data processed)

Figure 1. Development of Islamic Banking in Indonesia


The development of Islamic banking in Indonesia has become a measure of the success of the Islamic economy's existence. Bank Muamalat, as the first Islamic bank and a pioneer for other Islamic banks, has already implemented this system amidst the proliferation of conventional banks.


Based on the development of Islamic banking in Indonesia, the researchers are interested in researching all Islamic Commercial Banks (BUS) established as a unit. It is hoped that the establishment of a Sharia Commercial Bank (BUS) will help the banking world serve the needs of the public, especially those who are increasingly aware of the importance of interest-free banks.


The purpose of establishing a company is to maximize profits and maximize company voters or shareholders' prosperity. From these two objectives, the management must be able to generate optimal profits and careful control of operational activities, especially those related to finance, and be able to avoid loss conditions that ultimately lead to bankruptcy.


Financial distress is a situation in which a company's operating cash flow is not sufficient to meet current obligations (such as trading credit or paying interest). The term financial distress is used to reflect problems with the company's liquidity level (Putra, 2013). Internal factors include financial and non-financial factors, while external factors, namely the economic condition of a country or the global economy. Accounts receivable turnover is very low is also one of the causes of financial distress.


The importance of predicting an entity's going concerned is also because, according to the facts, the assumptions as above do not always come true. Companies that have been operating for a certain period are often forced to dissolve because of a decline in financial performance that leads to bankruptcy.


Bankruptcy is a phenomenon that often occurs in the business world. Default is usually defined as the failure of a company to carry out its operations to generate profits. The causes of bankruptcy can come from internal and external factors. Default is also often called company liquidity or company closure, or insolvency (Harahap, 2003).


Bankruptcy analysis is performed to obtain early bankruptcy warnings (early signs of bankruptcy). The more first the signs of default, the better for management because management can improve (Hanafi and Halim, 2009: 263).


The prediction of a company experiencing financial distress, which subsequently goes bankrupt, is one of the most important for interested parties such as creditors, investors, regulatory authorities, auditors, and management. For creditors, this analysis becomes the primary consideration in deciding their receivables, adding accounts receivable to overcome these difficulties, or taking other policies. Meanwhile, from the investor's point of view, the analysis results will be used to determine attitudes towards the securities owned in the company where he will invest.


The prediction of bankruptcy is not an isolated event but is a gradual process. Each of these stages requires an approach and an adverse mechanism to occur. An in-depth understanding of the prediction of a company's bankruptcy is essential to understand what will happen in a company.


Using the reference basis from Altman & Hotckiss (2006), a process can be drawn up so that the company will experience financial distress, as illustrated below.

Figure image is available in PDF Format

Figure 2. Financial Distress Cycle to Bankruptcy


From the picture above, we can see that the process leading to financial distress begins with its financial performance, which has decreased due to company performance. If the company's performance has continuously reduced for several years, it can enter into a condition of financial distress. Financially, at this stage, there is a possibility that the company will still be able to make payments for its obligations. Even when in financial distress, it is still possible for the company to pay its debts. However, if conditions worsen, the company will enter the next stage, namely default. This condition leaves the company in a position to have no longer the ability to pay third parties' obligations.


So the default position is a condition that involves the creditor. If this company's situation continues, the company will enter the worst state, which is bankruptcy. Bankruptcy is a legal condition in which a company has been declared bankrupt and is no longer able to pay third party obligations. This can happen in all industrial companies in all fields, both in manufacturing, services, conventional banking, and Islamic banking in Indonesia.


To avoid all the bad possibilities in a company in this financial distress, the company should immediately take swift and quick steps to restore the company's condition to its original state. If you have to wait until the company enters bankruptcy, then the chances of saving the company are minimal. Therefore, the existence of financial distress can be predicted early in this condition.


Prediction of financial difficulties in companies, including Islamic banking, can be done by looking at Islamic banking's financial ratios. The financial ratios are designed to build evaluating financial statements, namely to reveal the relative strengths and weaknesses of a company compared to other companies in the same industry and to show whether its financial position has improved or worsened over a particular time.


Financial ratio analysis can be used as a means/media to predict company bankruptcy to be very interesting, after Altman in 1968 found a formula to predict bankruptcy with the well-known term, namely the Z-Score. Practitioners widely use the use of the Altman Model in predicting the bankruptcy of a company.


In addition to the Altman (Z-Score) method, other models predict bankruptcy, namely The Zmijewski Model, The Springate Model, and Ohlson Model. The Zmijewski Model (X-Score) uses financial ratio analysis that measures the performance, leverage, and liquidity of a company for its prediction model. This model uses a probit analysis applied to 40 companies that have gone bankrupt and 800 companies that are still currently in business. The variables used in The Zmijewski Model are ROA, Debt Ratio (Leverage), and Current Ratio (Liquidity). The Springate Model (S-Score) uses a multi-discriminant analysis, with variables Working Capital to Total Asset, Net Profit before Interest and Tax to total assets, Net Profit before tax to current liabilities, sales to total assets.


The development of research on financial distress prediction models has progressed quite rapidly. This is evident from the emergence of other financial distress prediction models such as Springate (1978), Ohlson (1980), Zmijewski (1983), Fulmer (1984), Internal Growth Rate (1998) and Groever (2001). However, almost all of these models are formulated using a sample of companies abroad. Research on financial distress that exists in Indonesia so far still uses models developed from outside (already exist). The prediction model for financial distress that is mostly used in Indonesia is the Altman Z-Score model. In general, research in Indonesia is to compare existing models to assess the accuracy of the predictions.


Safitri & Hartono (2014), in their research entitled Application Test of Financial Distress Predictions Altman, Springate, Ohlson, and Zmijewski on Financial Sector Companies on the Indonesia Stock Exchange, shows the conclusion that a suitable model is used to predict the financial distress of financial sector companies listed on The Indonesia Stock Exchange is a Springate model. Another thing that makes the Springate model superior compared to the other three models in predicting financial distress in financial sector companies is the profitability variable used by the Springate model is earnings before interest and tax (EBIT), while in other models, especially the Ohlson model which has a level lowest accuracy, using the profitability variable in the form of net income after tax and interest or net income, due to high taxes and interest in financial sector companies so that it will affect the level of accuracy and calculation of each model.


Gunawan, Pamungkas, and Susilawati (2017) analyzed a financial distress prediction model with the title Comparison of Financial Distress Predictions with Altman, Grover, and Zmijewski Models. This study also took a sample of manufacturing companies listed on the Indonesia Stock Exchange (BEI). The results of this study indicate that the three comparable financial distress prediction models (Altman model, Grover model, and Zmijewski model) and Zmijewski model have the highest level of accuracy in predicting economic distress conditions based on the results of the determination coefficient test. The Zmijewski model has the highest Nagelkerke R square value among the three models tested. When compared with the other two models, the Zmijewski model equation has different characteristics. Zmijewski's model emphasizes the size of the debt, while the other two models emphasize the measure of profitability.


Hantono (2019) also researched by comparing the prediction model of financial distress with the research title Predicting Financial Distress using the Altman Score, Grover Score, and Zmijewski Score Models with a case study on the sub-sector of large trading companies listed on the Indonesia Stock Exchange (IDX). The results of this study indicate that the Altman Score and Grover Score models can be used as a reference for predicting financial distress in the large trading company sub-sector. In contrast, the Zmijewski score model is the least suitable model to be applied to large trading sub-sector companies.


Wulandari, Nur, and Julita (2014) conducted a study entitled Comparative Analysis of Altman, Springate, Ohlson, Fulmer, CA-Score, and Zmijewski Models in predicting Financial Distress with empirical studies on Food and Beverages Companies listed on the Indonesia Stock Exchange in 2010. -2012. The results of this study indicate that the Ohlson model is the most effective and accurate analytical model in predicting financial distress in Food and Beverages companies listed on the Indonesia Stock Exchange in the 2010-2012 period.


The level of prediction conformity produced by the Ohlson model is based on the results of hypothesis testing where the coefficient of determination and the significance value of the Ohlson model F is the highest value compared to other models used to predict financial distress conditions in Food and Beverages companies listed on the Indonesia Stock Exchange in 2010- 2012.


Based on the four studies mentioned above, it would be interesting if the researcher conducted a bankruptcy analysis in the Islamic banking sector in Indonesia using 3 (three) models, namely: the Altman method (Z-Score), The Zmijewski (X-Score), and Ohlson (Y-Score). ) to find out which Islamic Commercial Banks (BUS) in Indonesia has the potential to experience financial distress which can eventually lead to bankruptcy. This study's results can be used by Islamic banking to take preventive measures if an indicated Islamic bank is already in a condition towards bankruptcy.


The more it can be known from the start, the better it will be for management to make decisions. Management can immediately make preparations and improvements so that the company does not experience bankruptcy in the future. Also, for external parties, the company will see the prediction of the company's financial condition, which will be used as a basis, reference, and input in making financial decisions.


Literature Review

Financial Distress

Financial distress or financial difficulties can be interpreted as a company's inability to pay its financial obligations at maturity, which causes the company's bankruptcy (Darsono and Ashari, 2005 in Kartikawati, 2008). Financial distress is also defined as the stage of decline in economic conditions that occur before bankruptcy or liquidity (Platt and Platt, 2002).


The ups and downs in the business world do not make a company's business trip always show business development. Still, at one time, there are times when you experience severe financial difficulties. Companies' economic challenges can vary between liquidity difficulties in which the company is unable to fulfill its financial obligations for a while, to solvency difficulties (bankruptcy), where the company's financial obligations exceed its wealth. If the company's prospects are not hopeful, then liquidity will have to be pursued. However, many companies experiencing financial difficulties can be liquidated for the benefit of creditors, shareholders, and the public. Although the primary purpose of liquidity or rehabilitation is to protect creditors, company owners' interests are also considered. This condition can be said to be a symptom of financial distress.


Companies begin to experience financial distress when the company's operating cash flow is insufficient to meet short-term obligations, such as payment of interest on past-due loans. Several expressions are used to describe the company in a state of financial distress.


In general, company activities can be considered as a process of flow of funds. They are starting with withdrawing funds from various sources, then spending these funds on company assets, then carrying out operations on the company's assets, followed by reinvestment of funds obtained from company operations, and ending with a refund.


Approach Model for Predicting Financial Distress

Until now, research on financial distress prediction has developed both in the international world and in Indonesia. Of the many existing models, the researcher will describe several models that are considered the most popular to be used as predictive analysis, namely the Altman prediction model (Z-Score), The Zmijewski prediction model (X-Score), and the Ohlson model (Y-Score).


Altman Model (Z-Score)

Altman is the first person to implement Multiple Discriminant Analysis (MDA). Altman made the linear equation above as a refinement of Beaver's univariate research (1968). At that time, Beaver's research produced an equation that could only predict bankruptcy in a particular company by using accounting ratios not to be applied in general. Beaver's research's weakness was enhanced by Altman with his "Z-Score" using Fisher's (1936) discriminant analysis technique. The Z-Score result can predict the potential bankruptcy of a company continuously and is general.


The First Altman Model (1968)

Altman called the Z-Score for the first time was a linear model with financial ratios that were weighted to maximize the model's ability to predict bankruptcy. The sample used by Altman (1968) was 66 companies, namely 33 companies that were considered bankrupt and 33 companies that were healthy. Altman initially collected 22 company ratios that might be useful for predicting bankruptcy. Of the 22 proportions, tests were carried out to choose which rate to use in making the model. The selected rates are shown in the following equation:

Z-Score = 1,2 X1 + 1,4 X2 + 3,3 X3 + 0,6 X4 + 0,999 X5

X1 = Working Capital to Total Asset

X2 = Retained Earnings to Total Asset

X3 = Earning Before Interest and Taxes to Total Asset

X4 = Book Value of Equity/Book Value of Total Debt

X5 = Sales to Total Asset


The Z value is the overall index of the multiple discriminant analysis (MDA) function. According to Altman, there are cut-off figures for the Z value that can explain whether the company will fail or not in the future and divides them into 3 (three) categories, namely:

  1. If Z> 2.99 = "Safe" Zone (In this condition, the company is in a healthy state, so there is little chance of bankruptcy)

  2. If 1.81 <Z <2.99 = Zone "Gray" (In this condition, the company experiences financial distress, which must be handled with proper management. If it is too late, and the handling is not right, the company can go bankrupt. So in the gray area, this is the possibility of the company going bankrupt or the service from a period of financial distress)

  3. If Z <1.81 = Zone “Distress” (In this condition, the company experiences financial distress and is at high risk of experiencing bankruptcy).

  4. Revised Altman Model (1983)


The model developed by Altman underwent a revision. Altman's modification is an adjustment made so that this bankruptcy prediction model is not only applicable to manufacturing companies that go public but can also be applied to companies in the private sector. This old model changed in one of the variables used. Altman converts the Market Value of Equity numerator at X4 to Book Value of Equity because the private company does not have a market price for its equity. Here is a revised Altman model:


Z-Score = 0,717 X1 + 0,847 X2 + 3,108 X3 + 0,42 X4 + 0,988 X5

X1 = Working Capital to Total Asset

X2 = Retained Earnings to Total Asset

X3 = Earning Before Interest and Taxes to Total Asset

X4 = Book Value of Equity/Book Value of Total Debt

X5 = Sales to Total Asset


The classification of healthy and bankrupt companies is based on the Z-Score of Altman (1983) model, namely:

  1. If Z> 2.9 = "Safe" Zone (In this condition, the company is in a healthy state, so there is little chance of bankruptcy)

  2. If 1.23 <Z <2.9 = Zone "Gray" (In this condition, the company experiences financial distress, which must be handled with the right management. If it is too late, and the handling is not correct, the company can go bankrupt. So in the gray area, this is the possibility of the company going bankrupt or surviving from a period of financial distress)

  3. If Z <1.23 = Zone “Distress” (In this condition, the company experiences financial distress and is at high risk of experiencing bankruptcy).

  4. Modified Altman Model (1995)


Over time and adjustments to various types of companies. Altman then modified his model to be applied to all companies, such as manufacturers, non-manufacturers, and bond issuing companies in developing countries (emerging markets). In this modified Z-Score, Altman eliminates the X5 variable (sales / total assets) because this ratio varies in industries with different asset sizes. The following is the Z-Score equation modified by Altman et al. (1995):


Z-Score = 6,65 X1 + 3,26 X2 + 6,72 X3 + 1,05 X4

X1 = Working Capital to Total Asset

X2 = Retained Earnings to Total Asset

X3 = Earning Before Interest and Taxes to Total Asset

X4 = Book Value of Equity/Book Value of Total Debt


The classification of healthy and bankrupt companies is based on the modified Altman Z-Score model, namely:

  1. If Z> 2.6 = "Safe" Zone (In this condition, the company is in a healthy state, so there is little chance of bankruptcy)

  2. If 1.11 <Z <2.6 = Zone "Gray" (In this condition, the company experiences financial distress, which must be handled with the right management. If it is late, and the handling is not correct, the company can go into bankruptcy. So in the gray area, this is the possibility of the company going bankrupt or the service from a period of financial distress)

  3. If Z <1.11 = Zone “Distress” (In this condition, the company experiences financial distress and is at high risk of experiencing bankruptcy).

  4. Altman claims the formula's accuracy is 95% for a prediction period of 1 (one) year, with a potential error of between 10% - 15%.

  5. The Zmijewski Model (X-Score)


Zmijewski (1983) conducted an expansion of the study in predicting bankruptcy by adding the validity of financial ratios as a means of detecting corporate economic failures. Zmijewski was surveyed by re-examining the course of bankruptcy due to previous research for 20 (twenty) years. Several financial ratios were selected from last research financial ratios and a sample of 75 bankrupt companies, as well as 3573 healthy companies during 1972 to 1978, indicators of F-test against group ratios, Rate of Return, Liquidity, Leverage, Turnover, Fixed payment coverage, Trends, Firm size, and Stock return volatility, indicating a significant difference between healthy and unhealthy companies. The following is the model formulated by Zmijewski as follows:


X-Score = -4,3 – 4,5 X1 + 5,7 X2 – 0,004 X3


The financial ratios analyzed are the financial ratios contained in the Zjimewski model, namely:

X1 = Earning After Taxes of Total Assets x 100% (Return On Asset)

X2 = Total Debt to Total Asset x 100% (Debt Ratio or Leverage)

X3 = Current Asset to Current Liabilities (Current Ratio or Liquidity)


The Zmijewski model of company classification is based on a cut-off point value of 0 (zero). If the X-Score value is below the cut-off point, then the company is in a healthy condition. However, the X-Score is above the cut-off point, so the company is in financial distress.


Research Methods

Time and Location of Research

The research was conducted from February 2020 to June 2020. This research was conducted using secondary data, which is accessed from the respective websites of 11 (eleven) Islamic banking companies in Indonesia.


Research Design

Research design is a framework used to carry out research. Research design provides procedures for obtaining the information needed to compose or solve problems in research. Research design is the basis of research. Therefore, a good research design will produce effective and efficient analysis.

According to Suryana (2010: 16), research methods or scientific methods are procedures or steps in obtaining scientific or scientific knowledge. In other words, the research method is a systematic way to organize science. Referring to the form of research, its objectives, the nature of the problem, and its approach, there are four kinds of research methods: experimental methods, verification methods, descriptive methods, and historical methods.


Population and Sample

This research population is banking sector companies in Indonesia, namely Sharia Commercial Banks, with the research period, namely 2020. Meanwhile, for the calculation of financial distress predictions using Islamic banking's annual financial report data in the 2015-2018 period. The population is a generalization area consisting of objects/subjects with specific qualities and characteristics set by researchers to study and then draw conclusions (Sugiyono, 2014: 80). The selection of the Islamic banking sector in Indonesia as a population is based on consideration because most financial distress research takes manufacturing companies' object. These conventional banks have been listed on the Indonesia Stock Exchange. The researcher wants to test whether this research can obtain different results from studies conducted in traditional manufacturing and banking companies expected to be listed on the Indonesia Stock Exchange.


The research sample is used to get a picture of the population. The example is part of the people that you want to study. The model is a part of the whole and the characteristics of a population (Sugiyono, 2014: 118). The use of samples in this study is a company in the banking sector, namely Sharia Commercial Banks (BUS) in Indonesia.


Research Instruments

This research's research instrument is processed data from other sources obtained from various sources of books, journals, literature studies, previous research, the internet, and literature that supports this research. The research instrument is a data collection tool used to measure observed natural and social phenomena (Sugiyono, 2014: 92). This study describes how the comparative analysis of financial distress predictions uses the multiple discriminant method for all prediction models used in Islamic banking—research analysis using regression analysis.


Research Result

1. Analysis of Z Score Determination by Altman Model

Financial distress or financial difficulties can be interpreted as a company's inability to pay its financial obligations at maturity, causing the company's bankruptcy (Darsono and Ashari, 2005). Financial distress is also defined as the stage of decline in economic conditions that occur before bankruptcy or liquidity (Platt and Platt, 2002). One of the financial distress analyzes performed by Altman is known as the Z-Score.


The model developed by Altman underwent a revision. Altman's modification is an adjustment made so that this bankruptcy prediction model is not only applicable to manufacturing companies that go public but can also be applied to companies in the private sector. This old model changed in one of the variables used. Altman converts the Market Value of Equity numerator at X4 to Book Value of Equity because the private company does not have a market price for its equity. The following is the Revised Altman model:

Z-Score = 0,717 X1 + 0,847 X2 + 3,108 X3 + 0,42 X4 + 0,988 X5

X1 = Working Capital to Total Asset

X2 = Retained Earnings to Total Asset

X3 = Earning Before Interest and Taxes to Total Asset

X4 = Book Value of Equity/Book Value of Total Debt

X5 = Sales to Total Asset

The calculation results in this model can be seen in the following table.

Table 1. Z-Score Calculation Results in the Revised Altman model

No

Bank

Z-score

Zone

1

BCA Sharia

0,85746

distress

2

BNI Sharia

0,96219

distress

3

BRI Sharia

1,46837

gray

4

BUKOPIN Sharia

0,93806

distress

5

Bank Jabar Banten Sharia

0,91499

distress

6

Maybank Sharia

2,20367

gray

7

Bank Mega Sharia

0,86996

distress

8

Bank Muamalah

0,8583

distress

9

Panin Dubai

1,09275

distress

10

Bank Sharia Mandiri

1,08622

distress

11

BTPN Sharia

2,2661

gray

12

Bank Victoria Sharia

1,15966

distress

Source: data analyzed

The classification of healthy and bankrupt companies is based on the Z-Score of Altman (1983) model, namely:

  1. If Z> 2.9 = "Safe" Zone (In this condition, the company is in a healthy state, so there is little chance of bankruptcy)

  2. If 1.23 <Z <2.9 = Zone "Gray" (In this condition, the company experiences financial distress, which must be handled with the right management. If it is too late, and the handling is not correct, the company can go bankrupt. So in the gray area, this is the possibility of the company going bankrupt or surviving from a period of financial distress)

  3. If Z <1.23 = Zone “Distress” (In this condition, the company experiences financial distress and is at high risk of experiencing bankruptcy).

Based on the table above analysis, the bank status can be compiled whether it is safe, gray, and distressed in the following table.


Table 2. Bank conditions experiencing a safe zone, gray and distress

Zone

Account

Persentase

Secure

0

0,00

Gray

2

16,67

Distress

10

83,33

Source: data analyzed


Based on these data, it is known that the number of Sharia banks in the gray zone is 2 or 16.67%, while in the distress zone, there are ten banks of 83.33%. Sharia banks that experienced gray conditions were two banks or 16.67%. Meanwhile, there is no Sharia bank in a safe zone or 0%.


2. Analysis of the Zmijewski Model

Zmijewski (1983) conducted an expansion of the study in predicting bankruptcy by adding the validity of financial ratios as a means of detecting corporate financial failures. Zmijewski conducted a study by re-examining the study of bankruptcy due to previous research for 20 (twenty) years. Several financial ratios were selected from previous research financial ratios and a sample of 75 bankrupt companies, as well as 3573 healthy companies during 1972 to 1978, indicators of F-test against group ratios, Rate of Return, Liquidity, Leverage, Turnover, Fixed payment coverage, Trends, Firm size, and Stock return volatility, indicating a significant difference between healthy and unhealthy companies. The following is the model formulated by Zmijewski as follows:


X-Score = -4,3 – 4,5 X1 + 5,7 X2 – 0,004 X3


The financial ratios analyzed are the financial ratios contained in the Zjimewski model, namely:

X1 = Earning After Taxes of Total Assets x 100% (Return On Asset)

X2 = Total Debt to Total Asset x 100% (Debt Ratio or Leverage)

X3 = Current Asset to Current Liabilities (Current Ratio or Liquidity)


The Zmijewski model of company classification is based on a cut-off point value of 0 (zero). If the X-Score value is below the cut-off point, then the company is in a healthy condition. However, the X-Score is above the cut-off point, so the company is in financial distress. The results of the analysis with the Zjimewski model can be seen in the following table.

Table 3. Zjimewski analysis results (X-Score)

No

Bank

X-Score

Zone

1

BCA Sharia

-3,7213

distress

2

BNI Sharia

0,80466

non distress

3

BRI Sharia

-3,9085

distress

4

BUKOPIN Sharia

-3,1716

distress

5

Bank Jabar Banten Sharia

0,53893

distress

6

Maybank Sharia

-2,7537

distress

7

Bank Mega Sharia

0,37189

non distress

8

Bank Mumalah

-3,3839

distress

9

Panin Dubai

0,2034

non distress

10

Bank Sharia Mandiri

-3,5152

distress

11

BTPN Sharia

-3,7135

distress

12

Bank Victoria Sharia

-3,5717

distress

Source: data analyzed

Based on the data above, three companies did not experience distress or as much as 25.0%, and the remaining nine companies experienced distress or as much as 75.0%.

DISCUSSION

The carriage analysis using the revised Altman Z-Score model and the X-Score model from The Zmijewski have differences. This difference can be seen in the following table.


Table 4. Comparison of analysis results

Bank

Z-Score Analysis

X-score Analysis

Comparison

BCA Sharia

Distress

Distress

Same

BNI Sharia

Distress

non distress

Not the same

BRI Sharia

Gray

Distress

Same

BUKOPIN Sharia

Distress

Distress

Same

Bank Jabar Banten Sharia

Distress

Distress

Same

Maybank Sharia

Gray

Distress

Not the same

Bank Mega Sharia

distress

Non distress

Not the same

Bank Muamalah

distress

Distress

Same

Panin Dubai

distress

Non distress

Not the same

Bank Sharia Mandiri

distress

Distress

Same

BTPN Sharia

Gray

Distress

Not the same

Bank Victoria Sharia

distress

Distress

Sama

Source: data analyzed


Based on the data above, it is known that five banks have different analysis results or 41.67%, while 58.33% have the same analysis results. This difference is significant, so it can be said that the results of the bankruptcy analysis based on Altman's Z-Score and Zmijewski's X-Score differ significantly.


This research is similar to previous studies such as Asyikin, Chandrarin, Harmono (2018) and K. G. M. Nanayakkara1, A. A. Azeez (2015). Both of these studies used the Altman model and X-Score model analysis tools.


CONCLUSION

Based on data analysis using the Altman Z-Score analysis model, it is known that the number of Islamic banks in the gray zone is 2 or 16.67%, while in the distress zone, there are ten banks or 83.33%. Sharia banks that experienced gray conditions were two banks or 16.67%. Meanwhile, there are no Islamic banks in a safe zone or 0%.


The results of the X-Score analysis by Zmijweski's model showed that three companies did not experience distress or as much as 25.0%, and the remaining nine companies experienced distress or as much as 75.0%.


The comparative analysis result shows that five banks have different analysis results or 41.67%, while 58.33% have the same analysis results. This difference is significant, so it can be said that the results of the bankruptcy analysis based on Altman's Z-Score and Zmijweski's X-Score differ significantly.


SUGGESTION

The prediction of several Islamic banks that have operated in Indonesia shows that most of them have considerable potential for bankruptcy and need to be watched by Islamic banks. Efforts to avoid this bankruptcy can be made by improving financial performance, such as increasing bank revenues, minimizing affordable bank operating costs, and cooperating with other banks or Indonesian banks to increase their liquidity.


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