Analysis of Financial Distress Prediction of Coal Subsector Companies Registered in Indonesia Stock Exchange During the
Period of 2012-2019 Using Survival Analysis
Reyka Wanda Oktavian1*, Brady Rikumahu1
1 Management of Business in Telecommunication and Informatics, Telkom University, Bandung, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 May 2021 | Published: 1 June 2021
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Abstract: In 2018-2019, companies in the coal sub-sector experienced a significant decline in reference coal prices. The decline in prices was due to excess production and no increase in demand. Also, there are external factors that have caused the decline in coal prices, namely the slowdown in global economic growth, the issue of a trade war between China and America, import restrictions by China where Indonesia is the largest supplier and the issue of environmentally friendly issues to reduce carbon emissions in European countries. The existence of internal and external factors that influenced the decline in the average coal price (HBA) caused pressure on mining companies in the coal sub-sector. If the company cannot survive the decline in coal selling price. Then the company may experience financial distress.
This study aims to see how the influence of liquidity ratios, activity, leverage, management, managerial ownership, and audit committee on the financial distress of coal sub-sector companies listed on the Indonesia Stock Exchange for the period 2012-2019. This research method is a quantitative method using time series data. The sampling technique used was the purposive sampling technique. This study uses a survival analysis technique with a regression model used, namely the Cox Proportional Hazard regression model. The results showed that the variables of liquidity ratio, activity ratio, leverage ratio, company size, managerial agency costs, managerial ownership, and the audit committee could not predict or did not have a significant effect on financial distress in coal sub-sector companies listed on the Indonesia Stock Exchange for the period 2012-2019.
Keywords: cox proportional hazard, financial distress, survival analysis
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1. Introduction
Coal is a mining commodity that is a mainstay for Indonesia. Coal is used as a fuel for electricity generation. Besides, coal can also be used as gas fuel, support in various industries such as steel, aluminium, cement, and produces coal tar which can be used to make buildings waterproof, for insulation of buildings, making paint, cloth, shampoo, and making soap Apart from Indonesia, coal too is one of the main energy sources in the world. Based on a source from the page of The Daily Records, there are 10 coal-producing countries in the world, namely China, the United States, India, Australia, Russia, South Africa, Kazakhstan, Colombia, and Ukraine (Hasan, 2019). Apart from being a source of energy, coal is also a significant contributor to non-tax state revenue (PNBP) (Primadhyta, 2018).
In 2012-2019, PNBP of minerals and coal (Minerva) always exceeded the target already set.
However, in 2012-2019, the coal sub-sector experienced a decline in prices significant. The decline in prices was due to overproduction experienced an oversupply (excess supply) and demand did not increase in 2018-2019 (Saher, 2019). Besides, there are external factors that have caused the decline in coal prices, namely a slowdown global economic growth, the issue of the trade war between China and America, import restrictions by China where Indonesia is the largest supplier and there are issues about environmental friendliness to reduce the carbon emissions of European countries (Industri Kontan, 2018; sudutenergi.com, 2019). The existence of internal and external factors that affect the average coal price (HBA). This causes mining companies in the coal sub-sector to be depressed. If the company cannot survive in a condition where the selling price of coal is decreasing, then the possibility of the company experiencing financial distress. Research on financial distress prediction using survival analysis was also carried out by Tyaga & Kristanti (2020) with the research title “Analysis of Survival in Predicting Conditions Financial Distress”. The results of the study indicate that leverage, liquidity, activity, size companies, managerial agency costs do not have a significant effect on financial distress. Only inflation has a significant positive effect on financial distress.
So researchers interested in predicting the financial distress conditions of the coal sub-sector companies listed in The IDX for the 2012-2019 period in Indonesia are based on financial ratio analysis. In this study the ratio finance used is based on research conducted by Tyaga &
Kristanti, namely, the ratio liquidity is measured using Current Ratio (CR), the ratio of activities measured using Total Asset Turn Over (TATO), the leverage ratio is measured using the Debt to Equity Ratio (DER) and other research variables are firm size and managerial agency costs. On research in this case, the researcher also added managerial ownership and audit committee variables.
2. Literature Review
2.1 Company Performance
Company performance can be a barometer of a company's ability to achieve its goals (Febrianto, 2016:89). Measuring company performance is an important factor for companies because it measures the extent to which a company does work to achieve its goals. One way to measure company performance is through financial performance (Alifia & Rikumahu, 2020:969). Financial performance is a description of the state of a company as measured through financial analysis to determine whether the company is in good condition or otherwise within a certain period. Financial performance analysis, namely the process of determining the operating and financial characteristics of a company sourced from accounting and financial reports. Companies need to have the ability to analyse their financial position to be able to compete in the market (Pandian & Narendran, 2015:12141).
2.2 Financial Ratio
Financial ratios are calculations to explain the relationship between 2 financial data which are then interpreted so that they can be used for decision making (Salmah & Ermeila, 2018:124).
Financial ratios can be used to analyse the financial condition of a company. Investors are more interested in information on the company's short-term financial condition to pay adequate dividends (Fahmi, 2011:44).
2.3 Current Ratio
The current ratio is a ratio that shows the number of current liabilities the payment is guaranteed by current assets. The higher the result of the comparison of the number of current assets with
current liabilities, the higher the capability of companies in paying their short-term obligations (Hantono, 2018).
Current Ratio = Current Assets Current Liabilites
2.4 Total Asset Turn Over
Total Asset Turn Over (TATO) is a ratio to measure the level of efficiency in the use of all company assets to generate sales. Score a high TATO ratio indicates that the company can use all of its assets are used to get revenue for the company can increase company profits (Ariyanti
& Suwitho, 2016).
Total Asset Turn Over = Sales Total Assets 2.5 Debt to Assets
Debt to assets ratio (DAR) is a ratio to measure the assets used companies to guarantee all of their obligations (Hantono, 2018:13).
Debt to Assets Ratio = Total Debt Total Assets 2.6 Financial Ratio Analysis
Financial Ratio Analysis is a calculation to help evaluate financial reports. Calculation using this ratio is still the most effective way of measuring levels of the company's financial performance (Rhamadana & Triyonowati, 2016). Financial ratio analysis can be used as a tool to assess the performance and achievements of a company, besides financial ratio analysis is also beneficial for creditors to estimate the potential risks associated with sustainability guarantees repayment of loan principal along with interest payments (Fahmi, 2011:47).
2.7 Financial Distress
According to Fahmi (2011:93), financial distress is the stage where the company experienced a decline in financial condition before it occurred bankruptcy or liquidation of companies experiencing financial difficulties or financial distress due to insufficient amount of cash flow needed to fulfill its liabilities. Financial distress occurs when the company does not can pay its financial obligations at maturity if being left alone can lead to bankruptcy (Moleong, 2018:75).
2.8 Survival Analysis
Survival analysis is a collection of statistical procedures for analyzing data with variable results, namely the time until the event occurs (Kleinbaum & Klein, 2012:4-6). Survival time can be defined as the time from the onset of the event until the event fails. The initial time (time origin or start-point) is the time at the time of the initial event, such as the date of patient care, while the failure time (end-point) is the time at which the final event occurs such as the death or recovery of a patient (Collet, 2003).
2.9 The Cox Proportional Hazard Regression Method
Cox Proportional Hazard regression is a popular Cox proportional mathematical model used to analyze survival analysis data (Kleinbaum & Klein, 2012:100). Cox Proportional Hazard regression serves to determine the relationship between the dependent variable and the independent variable, where the data used in the Cox Proportional Hazards Regression is in the form of data on the survival time of an individual (Hari et al., 2018:2).
3. Methodology
3.1 Population and Sample
The population used in this study is the coal sub-sector companies listed on the IDX for the period 2012-2019. The sample used in this study amounted to 13 companies with sampling using the purposive sampling technique. Purposive sampling is a sampling technique using predetermined criteria based on research objectives so that the sample becomes a definite target to be taken (Syahrir et al., 2020:32). The sampling criteria in this study are coal sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2012-2019 period, coal sub- sector companies listed on the Stock Exchange (IDX) that do not issue complete company annual reports in the 2012-2019 period.
3.2 Data Analysis
A. Descriptive Statistical Analysis
To obtain data on research variables (liquidity ratios, activity ratios, leverage ratios, firm size, managerial agency costs, managerial ownership and audit committee) and earnings per share as measures in analyzing the financial distress of a company through secondary data has been provided by trusted parties such as their respective websites companies to get ratio data. The sample used in this study consisted of 13 companies with sample selection criteria, namely coal sub-sector companies listed on the Indonesia Stock Exchange (IDX) period 2012-2019 and coal sub-sector companies listed on the Indonesia Stock Exchange (IDX) which does not issue a complete company annual report on 2012-2019 period.
Table 1: Descriptive Statistical Results
Variable N Minimum Maximum Mean Std.Deviation Financial Distress 104 -456.16 77359.08 1912.5929 9241.58798
Liquidity Ratio 104 0.09 19.82 2.0881 2.27828 Activity Ratio 104 0.00 2.07 0.8950 0.51785
Leverage Ratio 104 0.01 1.89 0.4913 0.30607 Company Size 104 16.51 65.76 28.7389 6.20340 Managerial agency costs 104 0.00 34.46 0.6940 3.74328 Managerial ownership 104 0.00 8.70 0.2851 1.18621 Audit Committee 104 0.00 4.00 2.9231 0.79674 Valid (N) 104
Table 1 shows that N or the amount of data for each variable valid amounted to 104 out of 104 sample data for research variables and known results from descriptive statistics show the minimum value, maximum value, average (mean), and the standard deviation of each variable, among others:
The liquidity ratio indicated by the current ratio (CR) has a mean value of 2.0881 with a standard deviation value of 2.27828. The minimum value of 0.090 is owned by PT Bumi Resources Tbk in 2015 while the maximum value of 19.82 is owned by PT Tambang Batu Bara Bukit Asam Tbk in 2014. The activity ratio has a mean value of 0.8950 with a standard deviation value of 0.51785. The minimum value of 0.004 is owned by PT Bumi Resources in 2017 while the maximum value of 2.07 is owned by PT Resouces Alam Indonesia Tbk in 2012.
The leverage ratio has a mean value of 0.4913 with a standard deviation of 0.30607. The minimum value of 0.01 is owned by PT Harum Energy Tbk in 2017 while the maximum value of 1,890 is owned by PT Bumi Resources Tbk in 2016.
Company size has a mean value of 28.7389 with a standard deviation of 6.20340. The minimum value of 16.51 is owned by Toba Bara Sejahtra Tbk in 2014 while the maximum value of 65.76 is owned by the Bukit Asam Coal Mine in 2019. Managerial agency costs have a mean value
of 0.6940 with a standard deviation of 3.74328. The minimum value of 0.002 is owned by PT Delta Dunia Makmur Tbk in 2018 while the maximum value of 34.46 is owned by PT Adaro Energy Tbk in 2014. Managerial ownership has a mean value of 0.2851 with a standard deviation of 1.18621. The minimum value of 0.000005 is owned by PT Bukit Asam Tbk in 2015 while the maximum value of 8.70 is owned by PT Delta Dunia Makmur Tbk in 2017.
The audit committee has a mean value of 2.9231 with a standard deviation value of 0.79674.
The minimum value is 0 and the maximum value is 4. This value indicates the number of audit committees in the coal sub-sector companies listed on the Indonesia Stock Exchange for the period 2012-2019.
B. Testing Parameters Using The Likelihood Ratio Test (G Test)
Table 2: Likelihood Ratio Test Result
Likelihood Value The likelihood of the model without the predictor variable 34.671 The likelihood of a model consisting of predictor variables 24.480
Based on Table 2, the results of the Likelihood Ratio Test show that the value of the likelihood of the model without predictor variables is 34,671, while the likelihood value of the model consisting of predictor variables is 24,480.
Then the results for the calculation of the Likelihood Ratio test using the formula:
G = -2 [log L (0) - log L (β)] which is equal to -1.84 so there is no independent variable that has a significant effect on the dependent variable.
C. Testing the Regression Coefficient (Cox Proportional Hazard Regression)
Table 3: Cox Proportional Hazard Regression Result
B SE Wald df Sig. Exp(B) Liquidity Ratio -0.615 0.931 0.437 1 0.509 0.540 Activity Ratio 2.913 1.908 2.332 1 0.127 18.413
Leverage Ratio -5.216 5.595 0.869 1 0.351 0.005 Company Size -0.059 0.426 0.019 1 0.890 0.943 Managerial Agency Costs 0.414 0.404 1.054 1 0.304 1.514 Managerial Ownership -1.025 3.483 0.087 1 0.768 0.359 Audit Committee -0.040 1.605 0.001 1 0.970 0.961
Based on table 3, the significance value of all independent variables (liquidity, activity, leverage, company size, managerial agency costs, managerial ownership, and audit committee) is greater than 0.05, so there are no independent variables that can predict or have no significant effect on financial conditions the distress of the coal subsector company in 2012-2019. Also, based on table 3, there is a value from the Wald test results. According to Yolanda & Kristanti (2020), the Wald test is carried out to find out that each independent variable influences the dependent variable. This influence is seen using a significance value (α), namely 0.05. If the significance value is more than 0.05 (p-value> 0.05) then H0 is accepted, meaning that there is no significant effect between the independent variables on the dependent variable.
4. Conclusion
Based on the results of parameter testing using the likelihood ratio test (G test) that there are no independent variables (liquidity ratio, activity ratio, leverage ratio, company size,
managerial agency costs, managerial ownership, and audit committee) that have a significant effect on the dependent variable (financial distress). Based on the results of financial distress predictions using cox proportional hazard regression in coal subsector companies listed on the Indonesia Stock Exchange for the period 2012-2019, it shows that there is no liquidity ratio, activity ratio, leverage ratio, company size, managerial agency costs, managerial ownership, and audit committee which has a significant effect on financial distress conditions, then an increase/decrease in the value of these variables does not affect the possibility of the company experiencing financial distress.
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