scholarly journals An Analysis of Financial Distress Accuracy Models in Indonesia Coal Mining Industry: An Altman, Springate, Zmijewski, Ohlson and Grover Approaches

2021 ◽  
Vol 3 (2) ◽  
pp. 01-12
Author(s):  
M. Noor Salim ◽  
Dhermawan Ismudjoko
Keyword(s):  
Coal Mining ◽  
Predictive Models ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Mining Industry ◽  
Sampling Technique ◽  
Research Data ◽  
Accuracy Rate ◽  
Prediction Rate

The purpose of this research is to determine companies financial distress base on Altman, Springate, Zmijewski, Ohlson and Grover Models and to assess the accuracy of those five prediction models in coal mining sector firms listed in Indonesia Stock Exchange (IDX) for the period 2015 – 2019. This research has 22 samples of 23 coal mining firms listed in IDX base on the purposive sampling technique. This study is a descriptive design using quantitative and panel data. The research data is analyzed using the Kruskal Wallis test because there are more than two prediction models to compare and the data are not normally distributed. The result indicates that the Modified Altman and Ohlson Models are the most accurate predictive models because these models have the highest accuracy rate of 90.91%, followed by Zmijewski Model, which has an accuracy rate of 86.36%, then Grover Model has 81.82% accuracy rate, and the lowest prediction rate is Springate Model with the value of 63.64%.

scholarly journals Akurasi Model Logistik Springate, Zmijewski, dan Grover Dalam Menakar Kesulitan Keuangan Perusahaan Pembiayaan

2019 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Munawarah Munawarah
Keyword(s):  
Logistic Regression ◽  
Predictive Models ◽  
Financial Distress ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Total Sample ◽  
Annual Reports ◽  
Analysis Model ◽  
Purposive Sampling

This study aims to determine springate, grover and zmijewski able to predict the condition of financial distress in finance companies listed on the Indonesia Stock Exchange. From  three models can be known which model is the most accurate in predicting financial distress. There are 17 companies in Financing sector as population in this study from 2013-2017. Using purposive sampling technique, total sample of 85 financing companies was obtained. Secondary data were used in this research sourced from the company's annual reports. The analysis model used is logistic regression. Simultaneously, all predictive models affect the probability of financial distress. While partially only Zmijewski can influence the prediction of financial distress conditions in Financing sub-sector companies listed on the Indonesia Stock Exchange. Nagelkerqe Square value shows 0.606 meaning that only 60.6% variation of the accuracy of these three models in predicting financial distress conditions of finance companies. While 39.4% can be explained by other models not examined in this study.

scholarly journals Analisis Prediksi Kebangkrutan dengan Model Altman, Springate, Ohlson dan Zmijewski Pada Perusahaan Food and Beverage di Bursa Efek Indonesia (BEI)

2020 ◽  
Vol 2 (6) ◽  
pp. 901-909
Author(s):  
Munjiyah Munjiyah ◽  
Dwi Artati
Keyword(s):  
Prediction Models ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Financial Statements ◽  
Total Sample ◽  
Survey Result ◽  
Accuracy Rate ◽  
Food And Beverage ◽  
The Difference ◽  
One Way Anova

The purpose of this study was to determine and analyze the prediction of bancrupty are most appropriate for use in its application to the food and beverage company listed on the Indonesia Stock Exchange (BEI) period 2015-2018. The study compared four models of prediction of bancrupty, the Altman, Springate, Ohlson and Zmijewski. Comparisons were made by analyzing the accuracy of each models. Data used in the form of annual financial statements published by the company on the website www.idx.com. The sampling technique is purposive sampling with total sample acquired nine companies. The results of one way ANOVA test is significant as 0,000. So, there are the difference results of bancrupty prediction of food and beverage company using Altman, Springate, Ohlson and Zmijewski. The survey result revealed that the model Ohlson is a prediction models with the highest level of accuracy of 100%, with the type of error at 0%. The model of Zmijewski has an accuracy rate of 92,31% with the type error by 7,69%. While the model of Altman has an accuracy rate of 69,23%, with the type of error by 23,08% and the model of Springate has an accuracy rate of 69,23%, with the type error at 30,77%. Thus the accurate prediction models for food and beverage company registered in BEI is the Ohlson model, because have the best accuracy rate compared Altman, Springate and Zmijewski models.

scholarly journals Analysis of Financial Distress in Measuring Bankruptcy Before and After The Covid-19 Pandemic

2021 ◽  
Vol 1 (2) ◽  
pp. 53-60
Author(s):  
Supitriyani Supitriyani ◽  
Yansen Siahaan ◽  
Astuti Astuti ◽  
Juan Anastasia Putri ◽  
Elly Susanti
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Hypothesis Test ◽  
Sampling Technique ◽  
Accurate Method ◽  
Bankruptcy Prediction ◽  
Accuracy Rate ◽  
Government Regulations ◽  
Measurement Models ◽  
Before And After

The increasing spread of the Covid-19 virus at this time has forced several company sectors to experience setbacks in their operations. This epidemic has had a major impact, especially on the Transportation Sub-Sector Companies because they have to make some adjustments to government regulations such as implementing health protocols and physical restrictions on travel to break the chain of virus spread. The regulation has an impact on the company's revenue decline and the potency to suffer losses that can result in bankruptcy. This study aims to determine the bankruptcy prediction of the Transportation Sub-Sector Companies listed on the IDX before and after the covid-19 pandemic and to find out the most accurate method. The sampling technique used was non-probability sampling with the purposive sampling technique. The method used is descriptive with a quantitative approach. The results of the hypothesis test show that there are differences in predictions between the Altman and Springate models in predicting bankruptcy before and after the covid-19 pandemic. The Altman model is the most accurate prediction with an accuracy rate of 85.75%, while the Springate model has an accuracy rate of 73%. The study focused on companies listed on the IDX and used two bankruptcy measurement models, so researchers are next expected to use the entire company and other existing bankruptcy prediction, models. In addition, some factors beyond the control of researchers, such as economic conditions that cannot be measured. The renewal of previous research is to use two methods of prediction of bankruptcy, different objects, and research time (before and after the covid-19 pandemic).

scholarly journals ACCURACY OF SPRINGATE, ZMIJEWSKY AND GROVER AS LOGISTIC MODELS IN FINDING FINANCIAL DIFFICULTY OF FINANCING COMPANIES

ACCRUALS ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Munawarah Munawarah ◽  
Keumala Hayati
Keyword(s):  
Predictive Models ◽  
Financial Distress ◽  
Stock Exchange ◽  
Logistic Models ◽  
Secondary Data ◽  
Sampling Technique ◽  
Total Sample ◽  
Financial Difficulty ◽  
Analysis Model ◽  
Financial Reports

This study aims to determine both the Springate model, Grover and Zmijewski able to predict the condition of financial distress in finance companies listed on the Indonesia Stock Exchange. And of the three models can be known which model is the most accurate in predicting financial distress. The population in this study are companies in the financing sector listed on the Indonesia Stock Exchange in the period 2013 to 2017 as many as 17 companies. By using purposive sampling technique, a total sample of 85 financing companies was obtained. The data used are secondary data sourced from the company's annual financial reports. The analysis model used is logistic regression. Simultaneously, all predictive models for Springate, Zmijewski, and Grover affect the probability of financial distress. While partially only Zmijewski can influence the prediction of financial distress conditions in Financing sub-sector companies listed on the Indonesia Stock Exchange. Nagelkerqe Square value shows 0.606 meaning that only 60.6% variation of the accuracy of these three models in predicting financial distress conditions of finance companies. While the remaining 39.4% can be explained by other models not examined in this study

scholarly journals ANALISIS PERBANDINGAN PREDIKSI KONDISI FINANCIAL DISTRESS DENGAN MENGGUNAKAN METODE ALTMAN, SPRINGATE, DAN ZMIJEWSKI

2020 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fitri Listyarini
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Total Sample ◽  
Manufacturing Companies ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Pair Matching ◽  
Altman Model

This study aims to: 1) Determine the accuracy of the Altman model, the springate model and the zmijewski model in predicting financial distress conditions in manufacturing companies in Indonesia, 2) To find out the most accurate prediction models in predicting financial distress conditions in manufacturing companies in Indonesia. This study compares three financial distress prediction models, the Altman, Springate and Zmijewski models. The population of this study is the financial statements of manufacturing companies listed on the Indonesia Stock Exchange for the period 2011-2014. The sampling technique is pair matching sampling with a total sample of 28 companies, consisting of 14 companies experiencing financial distress and 14 companies not experiencing financial distress. Comparisons of the three financial distress prediction models are made by analyzing the accuracy of each model based on the company's real conditions. The results show that the zmijewski model is the most accurate model for predicting financial distress in manufacturing companies in Indonesia because it has the highest level of accuracy compared to other models, which is 100%, followed by the Springate model which has an accuracy rate of 89.29% and the Altman model by 75%.

The Effect of Managerial Ownership, Financial Performance, Corporate Competition on Stock Prices with Capital Structure as the Intervening Variable in the Coal Mining Companies Listed on the Indonesia Stock Exchange

2020 ◽  
Vol 2 (1) ◽  
pp. 24-33
Author(s):  
Yulia Afriani ◽  
Abdul Rakhman Laba ◽  
Andi Aswan
Keyword(s):  
Capital Structure ◽  
Financial Performance ◽  
Coal Mining ◽  
Stock Prices ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Managerial Ownership ◽  
Firm Competition ◽  
Mining Companies

This study aimed to find out the effect of managerial ownership, financial performance, corporate competition on stock prices with capital structure as the intervening variable in the coal mining companies listed on the Indonesia Stock Exchange. Managerial ownership variables by the shareholding presentation. Financial performance variables by Total Asset Turnover (TATO). Firm competition variable by Concentration Ratio (CR). Capital structure variables by Debt to Equity Ratio (DER). Stock prices variable by Price to Book Value (PBV). The population of this study was the coal mining companies listed on the IDX. This study used Purposive as the sampling technique. The data source was secondary data from financial statements published through the IDX official website. This study used descriptive statistics and inferential statistics with a quantitative approach using regression techniques with the E-Views version 10 program. The results of this study showed that the dealings of managerial ownership had a positive and significant effect on DER, TATO had a negative and not significant effect on DER, while CR had a negative and significant effect on DER. The dealings of managerial ownership, TATO, DER has a positive and significant effect on PBV, while CR has a negative and not significant. The dealings of managerial ownership influences PBV through DER, interestingly TATO has no effect on PBV through DER and CR influences PBV through DER

scholarly journals PENGARUH OPERATING CAPACITY, SALES GROWTH DAN ARUS KAS OPERASI TERHADAP FINANCIAL DISTRESS

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Annisa Livia Ramadhani ◽  
Khairun Nisa
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Financial Distress ◽  
Agricultural Sector ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Cash Flows ◽  
Sales Growth ◽  
Operating Capacity ◽  
Operating Cash Flows

This study aims to determine how the influence of operating capacity, sales growth and operating cash flows on financial distress. The population in this study israll agricultural sector companies listed on the Indonesia Stock Exchange (IDX) in 2013-2017. The sampling technique in this study used purposive sampling which produced 8 samples in a period of 5 years, namely as many as 40 units of data samples. The analytical method used is logistic �regression analysis which is processed. using SPSS Version 25. Based on the results of this study, it was found that simultaneous operating capacity, sales growth and operating cash flows influence the occurrence of financial distress. Then partially, operating capacity and sales growth have no effect on the occurrence of financial distress, while operating cash flows have a positive and significant effect on the occurrence of financial distress.�Keyword : Financial Distress, Operating capacity, Sales growth, Operation cash flow.

scholarly journals Profitabilitas Memoderasi Pengaruh Financial Distress pada Opini Audit Going Concern

2019 ◽  
Vol 29 (1) ◽  
pp. 420
Author(s):  
Anak Agung Gde Oka Maheswara ◽  
A.A. Ngurah Bagus Dwirandra
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Participant Observation ◽  
Binary Logistic Regression ◽  
Sampling Technique ◽  
Binary Logistic Regression Analysis ◽  
Manufacturing Companies ◽  
Audit Opinion ◽  
Going Concern ◽  
Observation Methods

The purpose of this study was to determine the effect of partial financial distress on the going concern audit opinion, to determine the effect of partial profitability on the going concern audit opinion and to know the moderating ability of profitability on financial distress that affects the going concern audit opinion. This research conducted at manufacturing companies listed on the Stock Exchange in 2015-2017. The research sample was obtained using purposive sampling technique. Data collection is done by non-participant observation methods. Data analysis techniques are carried out using the method of binary logistic regression analysis. The test results show that financial distress has an effect on the going concern audit opinion, profitability has no effect on the audit opinion, and profitability weakens the effect of financial distress on the going concern audit opinion. Keywords : Financial Distress; Going Concern Audit Opinion; Profitability.

scholarly journals Pengaruh Likuiditas, Leverage, Profitabilitas, dan Ukuran Perusahaan terhadap Financial Distress di Sektor Trade, Service, and Invesment

2021 ◽  
Vol 9 (3) ◽  
pp. 1227-1240
Author(s):  
Hasivatus Sariroh
Keyword(s):  
Firm Size ◽  
Financial Distress ◽  
Ordinal Data ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Annual Reports ◽  
Return On Assets ◽  
Current Ratio ◽  
Independent Variables

This study is a quantitative study that aims to determine the effect of the current ratio, debt to asset ratio, return on assets, and firm size on financial distress. Logistic regression method was used to test all relationships between independent variables and dependent variables with nominal/ordinal data scales. The dependent variable in this study is financial distress. The independent variables in this study are liquidity, leverage, profitability and firm size. This study uses secondary data from annual reports of trading, service, and investment companies listed on the Indonesia Stock Exchange from 2016 to 2018. The population used is companies in the trade, services, and investment sectors listed on the Indonesia Stock Exchange (IDX). from 2016 to 2018 with a total of 162 companies selected using purposive sampling technique. The results of hypothesis testing indicate that the current ratio, debt to asset ratio, return on assets, and firm size have no effect on the company's financial distress. From research conducted by researchers, for management to be used as a basis to take corrective actions if there are indications that the company experiencing financial distress. For investors, to be used as a basis in making the right decision to invest in a company.

scholarly journals Analisis Perbandingan Financial Distress Pada Perusahaan Sub Sektor Rokok Periode 2015-2019

2021 ◽  
Vol 3 (3) ◽  
pp. 157-163
Author(s):  
Anang Makruf ◽  
Deni Ramdani
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Market Value ◽  
Sampling Technique ◽  
Z Score ◽  
Book Value ◽  
Purposive Sampling ◽  
Quantitative Models ◽  
A Company ◽  
Analyze Data

Abstract – The aim of the study was to analyze financial distress in cigarette companies list in Indonesia Stock Exchange in 2015-2019 using 3 methods, Altman Z-Score, Zmijewski, and Springate. Purposive sampling is used in this study to determine the sampling technique. The sample used in this study released 4 cigarette companies. Descriptive asalysis with quantitative models was used to analyze data in this research. Altman Z-Score, Zmijewski, and Springate in 2015-2019 PT. HM Sampoerna Tbk, PT. Gudang Garam Tbk, and PT. Wismilak Inti Makmur Tbk is related to safe, but it is needed a company that is estimated to be grey in the Altman Z-Score calculation in 2018, PT. Wismilak Inti Makmur Tbk. The Z-score is at the limit because the companie has a ratio with a lower value in market value of equity  to book value of liabilities   Abstrak – Penelitian ini memiliki bertujuan untuk menganalisis perbandingan kesulitan keuangan dalam perusahaan sun sektor rokok di Indonesia Stock Exchange periode 2015-2019 menggunakan tiga metode. Metode yang digunakan yaitu Altman Z-Score, Zmijewski, dan Springate. Purposive sampling digunakan dalam penelitian ini untuk menentukan teknik pengambilan sampel. Sampel yang digunakan berjumlah 4 perusahaan rokok. Analisis deskriptif dengan pendekatan kuantitatif digunakan sebagai teknik analisis data. Dalam penelitian ini menjelaskan financial distress yang dihitung menggunakan metode Altman Z-Score, Zmijewski , dan Springate pada tahun 2015-2019 PT. HM Sampoerna Tbk, PT. Gudang Garam Tbk, dan PT. Wismilak Inti Makmur Tbk mengalami dalam kondisi keuangan yang sehat, namun terdapat perusahaan yang diestimasi rawan kebangkrutan pada perhitungan Altman Z-Score pada  tahun 2018 yaitu PT. Wismilak Inti Makmur Tbk. hal ini dapat terjadi  karena nilai Z-Score PT. Wismilak Inti MakmurTbk  berada pada Z < 1,81 salah satu penyebabnya ialah rendahnya rasio market value of equity terhadap liabilities.

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