The Effectiveness of The Altman Z-Score and Springate Methods in Analyzing The Potential for Company Bankruptcy

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The Effectiveness of The Altman Z-Score and Springate Methods in Analyzing The Potential for Company Bankruptcy

Mira Munira^1*, Indra Satria¹, Inggrid Qhurota Ayun¹

1 Faculty of Economic and Business, Universitas Pancasila, Jakarta, Indonesia

*Corresponding Author: [email protected] Accepted: 15 March 2021 | Published: 1 April 2021

__________________________________________________________________________________________

Abstract: This study aims to determine the potential bankruptcy per year using the Altman Modified Z-Score and Springate methods and to determine the level of accuracy for five years using the Altman Z-Score and Springate methods in measuring the potential bankruptcy of mining companies listed on the Indonesia Stock Exchange in 2015-2019. This study uses a quantitative descriptive method based on the Altman Z-Score and Springate models which are used to predict company bankruptcy. The data source in this study is secondary data obtained by downloading through the official website of the Indonesia Stock Exchange through www.idx.co.id with the sampling technique using purposive sampling. The results showed that overall in 2015-2019 the Altman Z-Score method had a better level of accuracy, 66.49% of the actual financial situation, while the Springate method only had an accuracy rate of 59.46% to the actual financial situation. according to the Indonesia Stock Exchange.

Keywords: Altman Z-Score, Springate, and Company Bankruptcy

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1. Introduction

The mining industry is one of the pillars of national economic development. This industry is run by mining companies that manage natural resources to be utilized for the development and welfare of the Indonesian people as in the 1945 Constitution Article 33 paragraph 3. The mining industry has long been a sector that generates Non-Tax State Revenue (Penerimaan Negara Bukan Pajak - PNBP). Mining sector non-tax state revenue is received from fixed fees (landrents), production fees (royalties) and sales of mining products. Non-tax state revenue is a source of revenue from the state treasury. This makes mining companies one of the main pillars of the national mining industry which has a significant role in the development of the industry in general.

Since 2005, when it surpassed Australian production, Indonesia has become the leading exporter of recognized quality coal. However, this situation changed when the global financial crisis occurred in 2008. This crisis resulted in a decline in global economic activity.

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Table 1: Coal Production, Foreign Sales, Domestic Sales, and Reference Coal Prices

Year Coal Production Foreign Sales Domestic Sales Reference Coal Prices

(Metric Ton) (Metric Ton) (Metric Ton) (USD/MT)

2011 255.729.964,61 192.319.857,11 57.460.353,89 118,4

2012 231.017.994,68 175.622.408,77 49.571.478,64 95,48

2013 272.046.445,46 207.066.377,97 57.442.713,35 82,92

2014 88.215.734,62 60.783.428,55 15.027.099,99 73,35

Source: esdm.go.id

Based on the table above, it shows that the Reference Coal Price continued to decline from 2012 to 2014 until it reached a value of 73.35 USD / MT which originally in 2011 reached a value of 118.4 USD / MT. This phenomenon continued until early 2017. The Indonesian Central Bureau of Statistics recorded that Indonesia's economic growth in the first quarter of 2017 grew by 5.01 percent compared to the first quarter of 2016 which was 4.92 percent. This growth was supported by almost all business fields except mining and quarrying which decreased by 0.49 percent. Compared to the fourth quarter of 2016 the Mining and Excavation sector experienced a decline of 0.78 percent. According to the Head of the Indonesian Central Bureau of Statistics, Suhariyanto, the decline was due to the decline in industrial growth in the coal and oil and gas sector to 2.8 percent compared to the first quarter of 2016 of 5.18 percent.

As for the non-oil and gas sector, it grew by 4.71 percent (Indonesian Central Bureau of Statistics, 2020).

The condition of the growth of Indonesian mining companies, which continues to experience a decline, will have an impact on the possibility of the company going bankrupt, which means the company's failure to run its operations to generate profits. Darsono and Ashari (2005) state that broadly the causes of bankruptcy are divided into two, namely internal factors and external factors. From external factors such as difficulty in raw materials because suppliers can no longer supply the raw materials used for production. Meanwhile, internal factors can be seen from a company's financial perspective, such as swelling corporate debt and negative working capital so that the company is unable to finance its operational activities.

Advance information about the condition of the company provides an opportunity for management, owners, investors, regulators and other stakeholders to make relevant efforts. It is in the interests of the management and owners to make efforts to prevent the condition from worsening into bankruptcy. Investors have an interest in making investment or divestment decisions. Regulators, such as Bank Indonesia and the Capital Market Supervisory Agency, carry out business supervision. In a situation like this, we need a way to predict whether a company is going bankrupt. Bankruptcy conditions can also be recognized earlier before they occur by using an early warning system model. The emergence of bankruptcy detection tools has been widely used, resulting in various bankruptcy prediction models that are used as a tool to improve the condition of the company before the company goes bankrupt. Several bankruptcy detection tools that are often used are the Altman Z-Score model and the Springate model.

This study aims to: (1) determine the potential bankruptcy per year using the Altman Z-Score modification method and the Springate method in mining sector companies listed on the Indonesia Stock Exchange for the period 2015-2019; (2) knowing the level of accuracy for five

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years using the Altman Z-Score and Springate methods in measuring the potential bankruptcy of mining companies listed on the Indonesia Stock Exchange for the period 2015-2019.

2. Literature Review

Bankruptcy Prediction Analysis Model - Altman Z-Score

Z-Score analysis, which is an analysis that connects various ratios in financial statements as variables and is combined into an equation to obtain a Z value, where the Z value is the value to predict the condition of the company, whether in good condition or bankruptcy with an accuracy level of 82% (McGough, 1974). The Altman Z-Score model as a measure of bankruptcy performance and bond risk is not stagnant or fixed, but evolves over time, in line with the condition of the company and the conditions in which the model is applied. The development of the Altman model can be seen starting from the first, namely the first Altman Z-Score model which is intended to predict the bankruptcy of a public manufacturing company.

After finding the first bankruptcy model, Altman then revised the bankruptcy model into a model that could be used to predict the likelihood of bankruptcy for private and public companies, this model is called the Altman Z-Score Revised Model. Furthermore, Altman modified his model so that it could be applied to all companies such as manufacturing, non- manufacturing and bond issuing companies. This model is called the Altman Z-Score Modified Model.

Several studies related to cases and the phenomenon of bankruptcy have been conducted.

Edward I. Altman (1968) was one of the earliest researchers who conducted this research.

Altman's research produced a formula called the Z-Score. General financial Z-Score analysis and different weighting (Nirmalasari, 2018). Altman selects 22 financial ratios, and in the end finds 5 ratios that can be combined to see which companies are bankrupt and not bankrupt, 5 types of ratios, namely : Working Capital to Total Assets, Retained Earning to Total Assets, Earning Before Interest and Taxes to Total Assets, Market Value of Equity to Book Value of Total Debt, and Sales to Total Assets.

The use of the Altman model as a measure of bankruptcy performance is not fixed but evolves over time, testing and model discovery continues to be expanded by Altman until its application is not only for public manufacturing companies but includes non-public manufacturing companies, non-manufacturing companies, and bond companies corporations (Nirmalasari, 2018). Following is the development of the Altman model:

A. First Altman Model

In the first study, Altman conducted research on various manufacturing companies in the United States that sell their shares on the stock exchange. So it is assessed that the first Z-Score formula is more suitable for predicting the business continuity of manufacturing companies that go public (Nirmalasari, 2018). The Altman I formula is known as the Z-Score, which is as follows:

Z = 1,2X1 + 1,4X2 + 3,3X3 + 0,6X4 + 1,0X5 Where :

X1 = Working Capital to Total Assets X2 = Retained Earning to Total Assets

X3 = Earning Before Interest and Tax to Total Assets X4 = Market Value of Equity to Book Value of Total Debt X5 = Sales to Total Assets

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The criteria used to predict company bankruptcy with this model are: (a) If the Z index value

<1.81, the company is predicted to go bankrupt (has the potential for bankruptcy); (b) If the Z index value> 2.99 then the company is not predicted to go bankrupt (the company is predicted to be good); (c) If the index value is 1.81 <Z <2.99, then it is a gray area (the company is predicted to experience financial problems and has the potential to go bankrupt). The cut off value for this index is 2,675. The first Altman model has a number of weaknesses to be applied to companies in various parts of the world with different conditions. These weaknesses include:

(a) This model only includes manufacturing companies that go public; (b) The first research conducted by Altman in 1968 certainly had conditions that were different from today's conditions, so the proportion of variables was less precise when reused (Nirmalasari, 2018).

B. Revised Altman Model

In 1984, Altman conducted research again in various countries. This study used various private manufacturing companies that did not go public or were not listed on the stock exchange (Nirmalasari, 2018). Altman then revised the first Z-Score model to the revised Altman model with the following formula:

Z’= 0,717X1+0,847X2+3.107X3+0,42X4+0,998X5

The criteria for a good and bankrupt company are based on the Z-Score value of the revised Altman model, namely (Nirmalasari, 2018): (a) If the Z index value '<1.23, the company is predicted to go bankrupt; (b) If the index value is 1.23 <Z '<2.9 then it is a gray area (the company is predicted to experience financial problems and will potentially go bankrupt); (c) If the index value for Z '> 2.9, it includes companies that are not bankrupt.

C. Modified Altman Model

As time goes by and adjustments to various types of companies, Altman conducts research again on the potential bankruptcy of companies other than manufacturing companies, both going public and not going public. The last Z-Score formula is a formula that is considered very flexible because it can be used for various types of company business fields and is suitable for use in developing countries such as Indonesia. This model is known as the Modified Altman model. In connection with the formula Z '' - Score modified Altman model, Altman eliminates the X5 variable (sales / total assets) because this ratio is very varied in industries with different asset sizes. The following is the Z 'formula - Score modified Altman model for various types of companies, as follows (Nirmalasari, 2018):

Z” = 6,56X1 + 3,26X2 + 6,72X3 + 1,05X4

The criteria for a good and bankrupt company are based on the Z-Score value of the Modified Altman model according to Nirmalasari, (2018), are: (a) If the Z index value is <1.1, the company is predicted to go bankrupt; (b) If the index value is 1.1 <Z "<2.6 then it is considered a grey area (the company is predicted to experience financial problems and will potentially go bankrupt); (c) If the index value of Z ”> 2.6 then this includes companies that are not bankrupt.

This study uses the Altman Z-Score modification method because this method is the newest method and the most suitable method for analyzing non-manufacturing companies.

Bankruptcy Prediction Analysis Model - Springate Analysis Model

Springate is a prediction model for financial distress in 1978. In its manufacture, Springate uses the same method as Altman, namely Multiple Discriminant Analysis (MDA). The Springate

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bankruptcy model uses 4 of 19 financial statement ratios that are widely used to distinguish between companies that experience financial distress and those that are not distressed (Vickers, 2005). The four ratios are the ratio of working capital to total assets, the ratio of profit before interest and tax to total assets, the ratio of profit before tax to current liabilities and the ratio of total sales to total assets. The four ratios are combined in an equation formulated by Springate with an accuracy rate of 92.5% (Purnajaya and Merkuisiwati, 2014).

According to Nirmalasari (2018), Springate made a prediction model for financial distress in 1978. In its creation, Springate used the same method as Altman, namely Multiple Discriminant Analysis (MDA). Like Beaver (1966) and Altman (1968), Springate (1978) initially collected popular financial ratios that can be used to predict financial distress. The initial number of ratios is 19 ratios. After going through the same test as that conducted by Altman (1968), Springate chose 4 ratios that are believed to be able to distinguish between companies that experience distress and those that are not. The sample used by Springate was 40 companies located in Canada. The model produced by Springate (1978) is as follows:

S = 1,03A + 3,07B + 0,66C +0,4D

Where:

A = Working Capital to Total Assets

B = Earnings Before Interest and Tax on Total Assets C = Profit Before Taxes Against Current Liabilities D = Sales to Total Assets

Criteria: Springate states that the applicable cut off value for this method is 0.861. A score that is smaller than 0.861 indicates that the company is predicted to experience financial distress.

However, if the score is greater than 0.861, it indicates that the company is not predicted to experience financial distress.

Sondakh, Murni, and Mandagie (2014) in their research entitled Analysis of Potential Bankruptcy Using the Altman Z-Score, Springate, and Zmijewski Methods in the Retail Trade Industry Listed on the IDX 2009-2013 period, it was found that the results of data processing with the three analysis methods were obtained the results are different from each other, and there are 3 companies that have the potential to go bankrupt in certain years. Thus, it is advisable for the three companies to improve their financial performance by increasing sales, reducing production costs and better understanding the current market situation.

Nirmalasari (2018) in his research entitled Financial Distress Analysis in Property, Real Estate and Building Construction Companies Listed on the Indonesia Stock Exchange stated that the Z-Score Modified Altman Method is the most accurate method for analyzing good financial distress when the economy is in moderate condition. bad or moderate good, the Altman Z- Score, Springate and Zmijewski Modified Altman method is more suitable for analyzing financial distress when a country's economic condition is good than when the economy is bad, the Springate method has the narrowest standard of determining the distress category, the second order is the method Altman Z-Score modification and the most extensive standard for determining the distress category is the Zmijewski method.

Agarwal and Patni (2019) state the results of research which show that the Springate and Zmijewski models are the best models that provide initial predictions and can be used to avoid losses due to investing money in bankrupt companies.

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Purwanti (2016) in her research states that the Altman Z Score model and the Springate model in analyzing company bankruptcy predictions have a significant difference. This could be due to differences in the variables in each model, where the Altman Z Score uses 5 variables, while the Springate only uses 4 variables.

3. Discussion and Conclusion

The data analysis technique used is quantitative analysis using financial ratios in the Altman Z-Score and Springate methods. Researchers intend to collect historical data and observe carefully about certain aspects that are closely related to the problem under study. The data obtained is then processed, further analyzed in order to obtain a description of the object of research and conclusions can be drawn about the problem under study. The steps used in the quantitative data analysis of this study are as follows:

A. Calculations using the Altman Modified Z-Score method through a formula (Nirmalasari, 2018):

Z” = 6,56X1 + 3,26X2 + 6,72X3 + 1,05X4

Table 2: Altman Z-Score Modification Method

Altman Z-Score Informations

<1,1 Financial Distress

1,1-2,6 Grey Area

>2,6 Non Financial Distress

Source: (Nirmalasari, 2018)

B. Calculations using the Springate method through the formula (Nirmalasari, 2018):

S = 1,03A + 3,07B + 0,66C +0,4D

Table 3: Springate Method

Springate Condition

< 0,861 Financial distress

> 0,861 Non Financial distress Source: (Nirmalasari, 2018)

C. Calculating the Accuracy Level of Altman Z-Score and Springate Methods

After calculating financial distress using these two methods, the next step is to calculate the level of accuracy in order to find out how much accuracy each method is in financial distress analysis. According to Nirmalasari, (2018), the level of accuracy is calculated as follows:

Level of Accuracy = the number of correct predictions

number of samples X 100%

The number of correct predictions is the number of samples of mining companies that do not experience financial distress and if calculated using the Altman Z-Score model, and Springate states the same thing as the Indonesia Stock Exchange statement. The number of samples is the number of companies sampled multiplied by the length of the observation year. After calculating the level of accuracy, the error or error rate is calculated for each model used in this

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study. How to calculate the error rate is divided into two types, namely Type I and Type II.

Type I error is an error that occurs when a model predicts that the sample under study does not experience financial distress, but in fact the sample is listed as a company experiencing financial distress. Meanwhile, Type II error is an error that occurs when a model predicts that the sample under study experiences financial distress, but in fact the sample is listed as a company that does not experience financial distress. The error rate is calculated as follows, (Nirmalasari, 2018):

Type I Error = number of type I errors

Number of Samples X 100%

Type II Error = number of type II errors

Number of Samples X 100%

The number of errors is the number of errors in the Altman Z-Score and Springate models in analyzing samples of mining companies that do not match reality.

Descriptive Statistics Results

The results of the statistical test in this study can be seen in the following table:

Table 4: Company Sample Data

Variable N Minimum Maximum Mean Std.

Deviation Working Capital to Total Assets (X1) 185 0.01 4.73 0.39 0.87

Profitability Ratio (X2) 185 (1.37) 3.71 0.15 0.45

Earnings Before Interest and Tax to

Total Assets (X3) 185 (1.54) 4.53 0.04 0.59

Book Value of Equity to Book Value of

Total Debt (X4) 185 (0.23) 9.23 1.10 1.60

Working Capital to Total Assets (A) 185 0.01 4.73 0.38 0.85

Earnings Before Interest and Tax on

Total Assets (B) 185 (1.54) 4.53 0.04 0.59

Net Profit Before Taxes against Current

Liabilities (C) 185 (9.03) 7.72 0.19 1.47

Sales to Total Assets (D) 185 0.01 4.97 0.50 0.73

Source: Processed secondary data

a. Working Capital to Total Assets (X1)

The minimum value of working capital to total assets is 0.01 and the maximum value is 4.73.

This shows that the amount of working capital to total assets in the sample of this study ranged from 0.01 to 4.73 with an average (mean) of 0.39 with a standard deviation of 0.87. The average value (mean) is smaller than the standard deviation, which means that the distribution of the value of working capital to total assets is not good.

b. Profitability Ratio (X2)

The minimum value of the profitability ratio is -1.37 and the maximum value is 3.71. This shows that the magnitude of the profitability ratio in the sample of this study ranges from -1.37 to 3.71 with an average (mean) of 0.15 at a standard deviation of 0.45. The average value

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(mean) is smaller than the standard deviation, which means that the distribution of the profitability ratio is not good.

c. Earnings Before Interest and Tax on Total Assets (X3)

The minimum value of profit before interest and tax on total assets is -1.54 and the maximum value is 4.53. This shows that the amount of earnings before interest and taxes on total assets in the sample of this study ranged from -1.54 to 4.53 with an average (mean) of 0.04 with a standard deviation of 0.59. The average value (mean) is less than the standard deviation, which means that the distribution of the value of earnings before interest and taxes on total assets is not good.

d. Book Value of Equity to Book Value of Total Debt (X4)

The minimum book value of equity to the total book value of debt is -0.23 and the maximum value is 9.23. This shows that the book value of equity to the total book value of debt in the sample of this study ranged from -0.23 to 9.23 with an average (mean) of 1.10 with a standard deviation of 1.60. The average value (mean) is smaller than the standard deviation, which means that the distribution of the book value of equity to the book value of total debt is not good.

e. Working Capital to Total Assets (A)

The minimum value of working capital to total assets is 0.01 and the maximum value is 4.73.

This shows that the value of working capital to total assets in the sample of this study ranged from 0.01 to 4.73 with an average (mean) of 0.38 with a standard deviation of 0.85. The average value (mean) is smaller than the standard deviation, which means that the distribution of the value of working capital to total assets is not good.

f. Earnings Before Interest and Tax on Total Assets (B)

The minimum value of earnings before interest and taxes on total assets is -1.54 and the maximum value is 4.53. This indicates that the value of earnings before interest and taxes on total assets in the sample of this study ranged from -1.54 to 4.53 with an average (mean) of 0.04 with a standard deviation of 0.59. The average value (mean) is less than the standard deviation, which means that the distribution of the value of earnings before interest and taxes on total assets is not good.

g. Net Profit Before Taxes against Current Liabilities (C)

The minimum value of net profit before tax against current liabilities is -9.03 and the maximum value is 7.72. This shows that the value of net income before tax on current liabilities in this study sample ranges from -9.03 to 7.72 with an average (mean) 0.19 at a standard deviation of 1.47. The average value (mean) is smaller than the standard deviation, which means that the distribution of the value of net income before tax on current liabilities is not good.

h. Sales to Total Assets (D)

The minimum value of sales value to total assets is 0.01 and the maximum value is 4.97. This shows that the value of net income before tax to current liabilities in this study sample ranges from 0.01 to 4.97 with an average (mean) of 0.50 with a standard deviation of 0.73. The average value (mean) is smaller than the standard deviation, which means that the distribution of the value of net income before tax on current liabilities is not good.

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Data Testing

The following are the results of bankruptcy prediction using the three Altman Z-Score and Springate Modification methods in the 2015-2019 period.

1) Data Calculation Results Altman Z-Score and Springate Modification Method

Based on research on 37 mining companies for the period 2015-2019 with the Altman Z-Score modification method using the Z equation "= 6,56X1 + 3,26X2 + 6,72X3 + 1,05X4 and after calculating each variable with the Z cut-off"> 2.6 is categorized as non-financial distress, Z

"1.1 <Z <2.6 then it is included in the gray area category, and Z" <1.1 is a category of financial distress companies. While the Springate method uses the equation model S = 1.03A + 3.07B + 0.66C + 0.4D and after calculating each variable with a cut-off S> 0.861 the company falls into the non-financial distress category, if S <0.861 the company is said to be in the financial distress category.

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Score Category Score Category Score Category Score Category

2015 3.12 Non - FD 0.84 Non - FD 2015 1.74 Grey area -0.03 FD

2016 3.56 Non - FD 1.1 Non - FD 2016 15.34 Non - FD 7.01 Non - FD

2017 4.13 Non - FD 1.53 Non - FD 2017 2.52 Grey area 0.73 FD

2018 10.85 Non - FD 2.39 Non - FD 2018 13.55 Non - FD 2.41 Non - FD 2019 10.21 Non - FD 1.97 Non - FD 2019 12.33 Non - FD 2.08 Non - FD

2015 3.08 Non - FD -0.15 FD 2015 6.8 Non - FD 1.46 Non - FD

2016 3.61 Non - FD 0.31 FD 2016 7.84 Non - FD 1.43 Non - FD

2015 2.81 Non - FD 0.63 FD 2015 1.35 Grey area -0.20 FD

2016 2.3 Grey area -0.11 FD 2016 -0.13 FD -0.56 FD

2017 1.61 Grey area -0.31 FD 2017 4.32 Non - FD 1.76 Non - FD

2018 -2.61 FD -0.75 FD 2018 5.04 Non - FD 0.98 Non - FD

2019 -0.33 FD 1.23 Non - FD 2019 7.3 Non - FD 1.56 Non - FD

2015 0.36 FD -0.11 FD 2015 35.32 Non - FD 14.83 Non - FD

2016 0.19 FD -0.03 FD 2016 6.31 Non - FD 1.81 Non - FD

2017 0.21 FD 0.14 FD 2017 4.98 Non - FD 2.09 Non - FD

2015 40.65 Non - FD 19.88 Non - FD 2015 15.06 Non - FD -5.73 FD

2016 34.92 Non - FD 11.88 Non - FD 2016 15.81 Non - FD -0.48 FD

2018 4.89 Non - FD 0.71 FD 2018 5.83 Non - FD 0.7 FD

2019 0.69 FD -1.69 FD 2019 4.14 Non - FD -0.79 FD

2015 2.54 Grey area 0.24 FD 2015 11.78 Non - FD -0.04 FD

2016 1.74 Grey area 0.51 FD 2016 9.27 Non - FD 0.98 Non - FD

2018 1.07 FD 0.12 FD 2018 11.28 Non - FD 1.74 Non - FD

2019 1.17 Grey area 0.2 FD 2019 13.88 Non - FD 1.68 Non - FD

2015 8.64 Non - FD 1.05 Non - FD 2015 6.66 Non - FD 0.57 FD

2017 15.74 Non - FD 1.80 Non - FD 2017 7.48 Non - FD 0 FD

2016 7.00 Non - FD 1.99 Non - FD 2016 1.14 Grey area -0.19 FD

2015 -0.18 FD -0.23 FD 2015 5.17 Non - FD 1.18 Non - FD

2016 0.76 FD 0.71 FD 2016 6.26 Non - FD 1.28 Non - FD

2015 8.5 Non - FD 1.54 Non - FD 2015 0.56 FD -0.24 FD

2015 1.55 Grey area 1.67 Non - FD 2015 -1.05 FD -2.13 FD

2016 1.97 Grey area 1.46 Non - FD 2016 3.33 Non - FD 0.57 FD

2017 2.49 Grey area 1.84 Non - FD 2017 3.42 Non - FD 0.56 FD

11 Citatah, Tbk. 24

Mitrabara Adiperdana,

Tbk.

13

Delta Dunia

Makmur, 26

Mitra Investindo, 12 Darma

Henwa, Tbk. 25

Medco Energy International,

Tbk.

10

Cita Mineral Investindo,

Tbk.

23

Resource Alam Indonesia,

Tbk.

9

Baya Resources,

Tbk.

22

Indo Tambangraya

Megah, Tbk.

8

Baramulti Suksessarana

, Tbk.

21 Indika Energy, Tbk.

7

Bumi Resources

Minerals, Tbk.

20

Vale Indonesia,

Tbk.

6

Astrindo Nusantara Infrastruktur

19 Harum Energy, Tbk.

5 Ratu Prabu,

Tbk. 18 Garda Tujuh

Buana, Tbk.

4

Atlas Resources,

Tbk.

17

Golden Energy Mines, Tbk.

3

Apexindo Pratama Duta, Tbk.

16 Energi Mega Persada, Tbk.

2

Aneka Tambang,

Tbk.

15 Elnusa, Tbk.

1 Adaro

Energy, Tbk. 14

Dian Swastatika Sentosa, Tbk.

Springate The Results of Financial Distress Analysis

No. Company Year Altman Z Score Springate

No. Company Year Altman Z Score

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Score Category Score Category Score Category Score Category

2015 2.54 Grey area -0.89 FD 2015 3.23 Non - FD 0.22 FD

2018 -3.39 FD -3.42 FD 2018 17.27 Non - FD 2.38 Non - FD

2015 2.7 Non - FD 0.7 FD 2015 3.73 Non - FD 0.43 FD

2016 2.67 Non - FD 0.58 FD 2016 4 Non - FD 0.53 FD

2017 2.41 Grey area 0.46 FD 2017 3.37 Non - FD 0.61 FD

2018 2.28 Grey area 0.31 FD 2018 1.08 FD 0.37 FD

2019 2.44 Grey area 0.66 FD 2019 0.15 FD 0.25 FD

2016 5.76 Non - FD 1.32 Non - FD 2016 4.73 Non - FD 1.29 Non - FD 2017 6.99 Non - FD 2.29 Non - FD 2017 4.43 Non - FD 1.55 Non - FD 2018 5.72 Non - FD 2.29 Non - FD 2018 18.39 Non - FD 3.96 Non - FD 2019 5.35 Non - FD 1.9 Non - FD 2019 14.44 Non - FD 3.03 Non - FD

2015 1.94 Grey area 0.09 FD 2015 -0.09 FD -0.94 FD

2016 2.04 Grey area 0.1 FD 2016 1.14 Grey area 0.23 FD

2017 13.2 Non - FD 0.42 FD 2017 2.09 Grey area 0.75 FD

2019 4.64 Non - FD 1.13 Non - FD 2019 14.81 Non - FD 1.95 Non - FD 2015 3.03 Non - FD 1.08 Non - FD

2016 3.39 Non - FD 1.03 Non - FD 2017 3.53 Non - FD 0.91 Non - FD 2018 4.66 Non - FD 1.15 Non - FD 2019 3.9 Non - FD 1.05 Non - FD No. Company Year Altman Z Score Springate

28

Perdana Karya Perkasa, Tbk.

27

Samindo Resources,

Tbk.

30 Bukit Asam, Tbk.

29

J Resources Asia Pasifik,

Tbk.

32

Radiant Utama Interinsco,

Tbk.

31 Petrosea, Tbk.

The Result of Financial Distress Analysis

No. Company Year Altman Z Score Springate

36

Toba Bara Sejahtera,

Tbk.

37 Trada Alam Minera, Tbk.

33

Golden Eagle Energy, Tbk.

34 SMR Utama, Tbk.

35 Timah, Tbk.

Source: Processed secondary data

The statistics in the table above show that the results of the analysis using the Altman Modified Z-Score in 2015, there were five companies that experienced financial distress. There are seven companies experiencing financial difficulties in the gray area. Twenty-five other companies fall into the category of companies with non-financial distress. In 2016, there were three companies that experienced financial distress. Meanwhile, there are seven companies experiencing financial difficulties in the gray area stage. Twenty-seven other companies fall into the category of companies that are healthy or non-financial distress. In 2017, there was one company that experienced financial distress, namely the company PT. Atlas Resources Tbk. There are seven companies experiencing financial difficulties in the gray area stage.

Meanwhile, the other twenty-nine companies are categorized as healthy companies (non financial distress). In 2018 there were four companies that experienced financial distress, and there were three companies that experienced financial difficulties at a mild level (gray area).

Meanwhile, as many as thirty companies were included in the category of healthy companies (non financial distress). Furthermore, in 2019 there were four companies that were declared to have financial distress, and there were two companies that experienced financial difficulties in a mild level (gray area). Meanwhile, the other thirty-one companies are healthy companies (non financial distress).

The results of calculations using the Springate method in 2015, there were twenty companies that experienced financial distress. Meanwhile, seventeen other companies are categorized as non-financial distress companies. In 2016, there were eighteen companies that experienced financial distress. Meanwhile, the other nineteen companies are categorized as non-financial distress companies. In 2017, there were fourteen companies that experienced financial distress.

Meanwhile, the other twenty-three companies are categorized as non-financial distress

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companies. In 2018 there were thirteen companies that experienced financial distress.

Meanwhile, the other twenty-four companies are categorized as non-financial distress companies. In 2019, there were nine companies experiencing financial distress. Meanwhile, the other twenty-eight companies are included in the category of healthy companies (non financial distress).

Comparison of Company Health Status According to Analysis Results

2015 Non - FD Non - FD Non - FD 2015 Non - FD Non - FD Non - FD

2016 Non - FD Non - FD Non - FD 2016 Non - FD FD FD

2017 Non - FD Non - FD Non - FD 2017 Non - FD FD Non - FD

2018 Non - FD Non - FD Non - FD 2018 Non - FD Non - FD Non - FD 2019 Non - FD Non - FD Non - FD 2019 Non - FD Non - FD Non - FD

2015 Non - FD FD FD 2015 Non - FD Non - FD Non - FD

2016 Non - FD FD Non - FD 2016 Non - FD Non - FD Non - FD

2016 Grey area FD FD 2016 Non - FD Non - FD Non - FD

2018 FD FD FD 2018 Non - FD Non - FD Non - FD

2019 FD Non - FD FD 2019 Non - FD FD FD

2015 FD FD FD 2015 Grey area Non - FD Non - FD

2015 Non - FD Non - FD Non - FD 2015 Grey area FD FD

2016 Non - FD FD FD 2016 FD FD FD

2017 Non - FD Non - FD Non - FD 2017 Non - FD Non - FD Non - FD 2018 Non - FD Non - FD Non - FD 2018 Non - FD Non - FD Non - FD 2019 Non - FD Non - FD Non - FD 2019 Non - FD Non - FD Non - FD 2015 Non - FD Non - FD Non - FD 2015 Non - FD Non - FD Non - FD 2016 Non - FD Non - FD Non - FD 2016 Non - FD Non - FD Non - FD 2017 Non - FD Non - FD Non - FD 2017 Non - FD Non - FD Non - FD 2018 Non - FD Non - FD Non - FD 2018 Non - FD Non - FD Non - FD 2019 Non - FD Non - FD Non - FD 2019 Non - FD Non - FD Non - FD

2015 FD FD FD 18 2015 Non - FD FD FD

2016 FD FD FD 2016 Non - FD FD FD

2018 Non - FD Non - FD Non - FD 2018 Non - FD FD Non - FD

2019 Non - FD Non - FD Non - FD 2019 Non - FD FD FD

No. Company Year Altman Z

Score Springate Conclusion No. Company Year Altman Z

Score Springate Conclusion

2

Aneka Tambang,

Tbk.

11 Citatah, Tbk.

1 Adaro

Energy, Tbk. 10

Cita Mineral Investindo,

Tbk.

4

Atlas Resources,

Tbk.

13

Delta Dunia Makmur,

Tbk.

3

Apexindo Pratama Duta, Tbk.

12 Darma Henwa, Tbk.

6

Astrindo Nusantara Infrastruktur

15 Elnusa, Tbk.

5 Ratu Prabu,

Tbk. 14

Dian Swastatika Sentosa, Tbk.

Bayan Resources,

Tbk.

8

Baramulti Suksessarana

, Tbk.

17

Golden Energy Mines, Tbk.

7

Bumi Resources Minerals,

Tbk.

16 Energi Mega Persada, Tbk.

Garda Tujuh Buana, Tbk.

Table 6: Company Status

9