scholarly journals Analysis of The Bankruptcy of Companies with Altman Model and Ohlson Model

2020 ◽  
Vol 9 (3) ◽  
pp. 243-251
Author(s):  
Andi Septian Najib ◽  
Dwi Cahyaningdyah
Keyword(s):  
Data Analysis ◽  
Financial Distress ◽  
Sampling Method ◽  
Prediction Models ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Processing Application ◽  
Ohlson Model ◽  
Altman Model

Often companies that have been operating for a certain period forced to disperse because of increased financial distress that caused bankruptcy. There are two models that can be used to predict bankruptcy of companies, that is Altman model (Z-score) and Ohlson model. This study aims to determine the accuracy of the Altman model (Z-Score) and Ohlson's model in predicting bankruptcy of delisting companies in Indonesia Stock Exchange for 2015-2019 period.The population in this study were all of delisting companies in Indonesia Stock Exchange for 2015-2019 period, totaled 17 companies. The number of samples used in this study were 8 companies, by using purposive sampling method. Data analysis used data processing application SPSS version 25. The results showed that accuracy of the Altman model is 58.3%, while the Ohlson model is 79.2%. The conclusion of this research Ohlson model has the highest accuracy that compared to Altman model in predicting bankruptcy at delisting companies in Indonesia Stock Exchange for 2015-2019 period, with accuracy values of Ohlson model is 79.2% and 58.3% for the Altman model. For further researchers, it is expected to increase the number of samples of companies studied and extend the research periods in order to provides more accurate results, and combining the Altman and Ohlson models with other bankruptcy prediction models that can be applied in companies in Indonesia.

scholarly journals Stock Prices Predicted by Bankruptcy Condition?

2018 ◽  
Vol 9 (2) ◽  
pp. 105-114
Author(s):  
Irawati Junaeni
Keyword(s):  
Stock Prices ◽  
Financial Distress ◽  
Stock Price ◽  
Sampling Method ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Analysis Method ◽  
Simple Regression Analysis ◽  
Score Model

This research had two objectives. First, it determined the prediction of the method of Altman Z-Score whether it could classify banking positions, bankruptcy, or financial distress in the go-public bank in Indonesia Stock Exchange. Second, it was to know the influence of value position of Altman Z-Score on the stock price. The population was 84 banking company listed on the Indonesia Stock Exchange in 2010-2015. The sampling method was purposive sampling. Moreover, data analysis method used was a simple regression analysis. For data processing, it used software Eviews 8. The Z-Score calculations predict the potential bankruptcy of go-public bank in 2010-2015. All results show that Z-Score has the small score of 1,81. It can be said there is a potential bankruptcy. For t-test, it can be concluded that Z-Score has the positive and significant effect on the stock price. The ability of Z-Score values in explaining the stock price is 95,50% while the remaining 4,50% is influenced by other variables that are not analyzed in the research. With some weaknesses of Altman’s Z-Score model, this research has the implication for management bank. It improves the financial performance for the future to avoid opportunity bankruptcy prediction. The results show how the effect of bankruptcy on banking stock prices.

scholarly journals Laporan Keuangan dan Prediksi Kebangkrutan Perusahaan

2020 ◽  
Vol 5 (1) ◽  
pp. 196
Author(s):  
Putu Riesty Masdiantini ◽  
Ni Made Sindy Warasniasih
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Prediction Models ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Analysis Techniques ◽  
Accuracy Level ◽  
Altman Model

This study aims to determine differences in bankruptcy predictions at company’s sub-sector of cosmetics and household listed on the Indonesia Stock Exchange (IDX) using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model, and to determine the bankruptcy prediction model that is the most accurate of the five bankruptcy prediction models. This study uses secondary data in the form of company financial statements for the period 2014-2018. Data analysis techniques in this study used the Kruskal-Wallis test. The results showed there were differences in bankruptcy predictions using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model. The Zmijewski, Taffler, and Fulmer models have the same accuracy level of 100% so that the three prediction models are the most accurate prediction models for predicting the potential bankruptcy at companies sub-sector of cosmetics and household listed on the IDX.

scholarly journals ANALISIS ALTMAN Z-SCORE, GROVER SCORE, SPRINGATE, DAN ZMIJEWSKI SEBAGAI SIGNALING FINANCIAL DISTRESS (Studi Empiris Industri Barang-Barang Konsumsi di Indonesia)

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Niken Savitri Primasari
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Stock Exchange ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
T Test ◽  
Z Score ◽  
F Test ◽  
Company Sector ◽  
Altman Model

This study purpose to determine whether there are differences among Altman model, Springate model and Zmijewski model to predict financial distress, and to find out which the Financial Distress prediction model has the most excellent implementation in Indonesia manufacture industry. Comparison of those six models were made by analyzing the accuracy of each model, by using the real condition of a company’s net income. The data used in the form of annual financial statements published by the company on the Indonesia Stock Exchange website. The sample in this study consisted of 116 financial data from 29 companies in Consumer Goods Industry. All companies are listed in Indonesia Stock Exchange Market at period 2012 - 2015. The company does not conclude yet, whether there is a prediction model that best suit the measurement. This cause by: (1) every model have its own superiority and weakness, (2) the company sample characteristic differences (company sector, company size) also influence the choice of prediction model being used, (3) the company financial ratio as independent variable used in bankruptcy prediction. Since the financial statements are reflecting the company’s financial ability of the signaling, the researchers limited the industry with the highest value of EPS and PER. This is done to avoid confounders in the proof of the accuracy of the model, Springate model, Ohlson model and Zmijewski model to predict financial distress. The data obtained from the Annual Financial Statements, IDX Fact Book and the Indonesian Capital Market Directory. In this study will be used t test, additional testing is done to see the feasibility of the model by observing the F test results and test the coefficient of determination (R2), R2 value used to examine differences among Altman, Grover, Springate and Zmijewski models in predicting financial distress. The analytical tool used is the One Way ANOVA with level of significance 5 %. The results from this research showed that any prediction model used in this study can be used to predict Financial Distress, particularly the Altman Z-Scores, which have the greater R2 analysis. Only Grover G-Score models have insignificant value t test and F-test is greater than the probability cannot be used to predict corporate Financial Distress. The results also showed that the most accurate model is the model Altman Z-Score. At the end of the study was to try predict 29 firms sample used listed on the Stock Exchange with Altman model. Predicted results showed that five companies are expected to experience Financial Distress in the future.

scholarly journals ANALISIS FINANCIAL DISTRESS DENGAN MENGGUNAKAN BANKRUPTCY PREDICTION MODELS PADA EMITEN ROKOK PERIODE 2014-2018

2021 ◽  
Vol 4 (1) ◽  
pp. 16-27
Author(s):  
Ani Wahyuningsih ◽  
Hartono Hartono ◽  
Rini Armin
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Correct Prediction ◽  
Financial Health ◽  
Z Score ◽  
Financial Difficulties

ABSTRACT Financial Distress is a condition of financial difficulties where if this happens to the company foa along period of time, the company is in the initial stages before bankruptcy. Bankruptcy is a state of being or a situation in which company failed to or not able to meet obligations because firm experienced lack of. If the company goes bankrupt there will be many parties who are harmed. Therefore it is necessary to conduct financial distress analysis for early warning. The research aims to determine the financial health of the cigarette sub-sector companies by analyzing financial distress using three bankruptcy prediction models with Altman Z-Score, Springate, Grover and to determine which of these three models has the highest level of accuracy. The data used in this research is the company’s financial statements published on the Indonesia Stock Exchange website. The population in this research is the cigarette sub-sector companies listed on the Indonesia Stock Exchange in the 2014-2018 period. Based on the result of research shows that in the calculation Altman and Springate models, PT. Bentoel International Investama in the category of the company experiencing symptoms of bankruptcy. While in the Grover model calculation, all companies fall into category healthy companies. Of the three models that have the highest level of accuracy are Altman and Springate models by one hundred percent. This shows that Altman and Springate models have the correct prediction of the company correctly.

scholarly journals PENGARUH FIRM SIZE, FINANCIAL DISTRESS, DEBT LEVEL, DAN MANAGERIAL OWNERSHIP TERHADAP KEPUTUSAN HEDGING PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Husna Anniyati ◽  
Hermanto Hermanto ◽  
Siti Aisyah Hidayati
Keyword(s):  
Data Analysis ◽  
Firm Size ◽  
Financial Distress ◽  
Sampling Method ◽  
Stock Exchange ◽  
Financial Statements ◽  
Managerial Ownership ◽  
Manufacturing Companies ◽  
Debt Level ◽  
Analysis Technique

This study aims to analyze the influence of firm size, financial distress, debt level, and managerial ownership on hedging decisions on manufacturing companies listed on the Indonesia Stock Exchange. This type of research is associative-causality research. The population of this research is all the go pubic manufacturing companies on the Indonesia Stock Exchange, which are 170 companies. The number of samples used was 81 companies, which were taken using a purposive sampling method. Data collection techniques use documentation techniques obtained from the annual financial statements of manufacturing companies. The data analysis technique uses the logistic regression analysis method. The results of data analysis show that: (1) firm size and managerial ownership variables have a positive and significant effect on hedging decisions and (2) financial distress and debt levels have a negative and insignificant effect on hedging decisions.Keywords:hedging, firm size, financial distress, debt level, managerial ownership

scholarly journals PENERIMAAN OPINI AUDIT GOING CONCERN BERDASARKAN LEVERAGE DAN FINANCIAL DISTRESS

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Huda Aulia Rahman
Keyword(s):  
Regression Analysis ◽  
Financial Distress ◽  
Sampling Method ◽  
Stock Exchange ◽  
Z Score ◽  
Mining Sector ◽  
Going Concern ◽  
Negative Effect ◽  
The Impact ◽  
Research Period

AbstractThe purpose of this study is to determine the impact of leverage and financial distress on going concern audit opinions of mining sector companies listed on the Indonesia Stock Exchange (IDX) with the 2012-2017 research period. Leverage was proxied using debt to asset ratio (DAR), and financial distress was proxied using Altman Z-Score. The object of this research used were 17 mining sector companies selected based on the random purposive sampling method. This study used data processing logistic regression analysis using SPSS version 25. The results of this research was financial distress have negative effect on going concern modification opinions, while leverage had no effect on going concern audit opinion.                         Keywords: Financial Distress, Leverage, Going Concern Audit Opinion  

scholarly journals Application of Altman Modified Z-Score to Predict Financial Distress in the Indonesian Telecommunications Industry

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Rianti Fifriani ◽  
Perdana Wahyu Santosa
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Financial Statements ◽  
Telecommunications Industry ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Purposive Sampling ◽  
Modification Method ◽  
The Future ◽  
The Right

Bankruptcy prediction is needed to assess the prospect of going concern and sustainability of the corporations in the future. This study aims to predict the bankruptcy of corporates with the Altman Z-Score Modification model in the telecommunications industry in Indonesia. The data used are the financial statements of the telecommunications industry that listing on the Indonesia Stock Exchange for the period 2011-2015. Samples for this study uses purposive sampling according to company criteria. The results of the study using the Altman Z-score modification method found two potentially bankrupt companies, namely Bakrie Telecom, Tbk, and Smartfren, Tbk. While Indosat, Tbk, and XL Axiata, Tbk have high financial distress potential due to liquidity and profitability problems that tend to weaken. Meanwhile, Telkom Indonesia, Tbk, and Infracom Inovisi financial concessions are relatively healthy and have the right business expectations

scholarly journals ANALISIS PENILAIAN FINANCIAL DISTRESS MENGGUNAKAN MODEL ALTMAN (Z-SCORE) PADA PERUSAHAAN FARMASI YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2013-2015

2017 ◽  
Vol 5 (1) ◽  
pp. 55
Author(s):  
Sri Yati ◽  
Katarina Intan Afni Patunrui
Keyword(s):  
Financial Distress ◽  
Sampling Method ◽  
Stock Exchange ◽  
Secondary Data ◽  
Pharmaceutical Companies ◽  
Z Score ◽  
Financial Reports ◽  
Book Value ◽  
Total Debt ◽  
Score Model

This study aims to observe the financial distress assessment for pharmaceutical companies listed on the Indonesia Stock Exchange using the Altman Z-Score model. The sample is selected using purposive sampling method. Ten pharmaceutical companies were selected with the criteria listed in the Indonesia Stock Exchange (BEI) and regularly published financial reports in 2013 until 2015. Secondary data was derived from www.idx.co.id site.  The results indicate that the Altman Z-Score model can be implemented in detecting the possibility of financial distress in the pharmaceutical company. Working capital to total assets and book value equity to book value of total debt are two determinant variables which is determining the decrease in Z-score value in this research.  One from ten companies have the lowest value of the Z-Score and experiencing financial distress. For two years, the company is in distress zones but in the third year, the company is managed to increase the value of the company and included in the gray zones. This company must continue to strive in order to stabilize the company's financial and asset utilization to obtain maximum profit, and until it was declared as a healthy company.

scholarly journals Likelihood of Auditor Switching: Evidence for Indonesia

2019 ◽  
Vol 8 (2) ◽  
pp. 29-40
Author(s):  
Hadri Kusuma ◽  
Diana Farida
Keyword(s):  
Data Analysis ◽  
Financial Distress ◽  
Sampling Method ◽  
Stock Exchange ◽  
Secondary Data ◽  
Certified Public Accountant ◽  
Manufacturing Companies ◽  
Auditor Switching ◽  
Correlation Tests ◽  
Public Accountant

This research aims to analyze factors determining the likelihood of auditor switching. The populations in this study were manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2015-2017. The type of data collected in this research was secondary data. The data analysis used in this research were 133 selected companies after applying purposive sampling method. The methods of data analysis were descriptive statistics, correlation tests, and generalized linear model (GLM). The results of this research indicated that the variables of financial distress, profitability, Certified Public Accountant (CPA) reputation and management change are significantly determinants of the likelihood of auditor switching. The paper further discusses and interprets the finding of the study.

FINANCIAL DISTRESS PREDICTION AND SEEING THE INFLUENCE OF THE ALTMAN RATIO ON COMPANY FINANCIAL DISTRESS

2018 ◽  
Vol 23 (3) ◽  
pp. 236-243
Author(s):  
Hadhi Dharmaputra Juliyan ◽  
Bertilia Lina Kusrina
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Large Role ◽  
Corporate Bankruptcy ◽  
Mining Companies ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Model

This research aims to determine the level of the bankruptcy of the company and to see if the Altman ratio can predict the condition of corporate bankruptcy in mining companies on the Indonesia Stock Exchange because mining companies have a large role in the Indonesian economy. This study uses the Altman Z-Score model analysis to see how much the company's bankruptcy prediction and uses logistic regression to see how much the influence of the Altman ratio in predicting corporate bankruptcy. Keywords: financial distress, the Altman z–score, bankruptcy prediction

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