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 Perbandingan Model Prediksi Kebangkrutan Altman Z-Score dan Zmijewski di BEI Periode 2011 - 2015

2019 ◽  
Vol 6 (2) ◽  
pp. 171
Author(s):  
Nindya Ayu Damayanti ◽  
N. Nurhayati ◽  
Susanti Prasetyaningtyas
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Z Score ◽  
Study Population ◽  
A Company

This study aimed to compare the use of bankruptcy prediction model Altman Z-Score and Zmijewski on delisting companies on the Stock Exchange the period 2011 - 2015. The study population is a company delisting from the Stock Exchange in the period 2011-2015. The sample consists of 7 companies using method. purposive sampling Secondary data used in the form of financial statements of companies that issued from stock for bankruptcy in the period 2011-2015. The data analysis in this research is to perform the calculation of financial ratios in each sample, according to the variables of bankruptcy prediction model were compared to the model of Altman Z-Score and Zmijewski. Furthermore, the company classifies conditions appropriate point cut-off of each model and did calculations the accuracy of each model. Keywords: Altman Z-Score, Delisting, Bankruptcy, Zmijewski.

Prediksi Kebangkrutan Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 126-141
Author(s):  
Melati Eka Putri ◽  
Auliffi Ermian Challen
Keyword(s):  
Prediction Models ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Analytical Models ◽  
Ratio Analysis ◽  
Debt Capital ◽  
Score Model

This study aims to examine the potential for bankruptcy of companies with three analytical models, namely Altman Z-Score, Springate S-Score, and Zmijewski X-Score, and assess the level of accuracy of the three models. Each model uses ratio analysis with the elements of assets, debt, capital, and company profits. This study uses a sample of coal mining companies listed on the Indonesia Stock Exchange (IDX) during the 2014-2018 period. The sampling technique in this study used purposive sampling and obtained 24 sample companies. This study uses secondary data, namely the company's financial statements obtained from IDX's official website. This study calculates financial ratios, compares the scores of the three bankruptcy prediction models, and tests the model's accuracy. The results of this study show that of the three models, the Springate S-Score model is the most accurate in predicting bankruptcy, with an accuracy rate of 83.33%, as evidenced by two companies that were delisted from the IDX. This study can be used as a reference and as material for consideration in making investment decisions for companies and investors.

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 OPINI AUDIT GOING CONCERN: KAJIAN BERDASARKAN MODEL PREDIKSI KEBANGKRUTAN, PERTUMBUHAN PERUSAHAAN, LEVERAGE DAN REPUTASI KANTOR AKUNTAN PUBLIK

2017 ◽  
Vol 13 (1) ◽  
pp. 79
Author(s):  
Farid Muhamadiyah
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Stock Exchange ◽  
Binary Logistic Regression ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Binary Logistic Regression Analysis ◽  
Manufacturing Companies ◽  
Audit Opinion ◽  
Going Concern

<p>Going-concern audit opinion is the auditor’s opinion regarding the ability of<br />the entity to maintain the viability of their business is one of the important things to<br />consider users of financial statements to make decisions especially berinvestas<br />decisions. This study aimed to examine the effect of bankruptcy prediction models (Altman revised model), growth companies (earnings), leverage and reputation of the public accounting firm of the admission trends going concern audit opinion. The sample used in this study consisted of 32 financial statements of listed manufacturing companies in Indonesia Stock Exchange (IDX) during the period 2007-2010. The sample was selected by using the purposive sampling method. In this research, data analysis using SPSS by binary logistic regression analysis to test the hypothesis. From the analysis in this study suggests that the use of bankruptcy prediction model (Altman revised model) positive effect on revenue trends going concern audit opinion, while the company’s growth, leverage and reputable CPA firm negatively affect revenue trends going concern audit opinion.<br />Keywords : Bankruptcy Prediction Model (Altman revised model), Corporate<br />Growth (income), Leverage, Reputation Public Accountant and Going Concern Audit Opinion.</p>

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 Faktor Determinan Return on Asset pada Bank BUMN yang Terdaftar di Bursa Efek Indonesia

2019 ◽  
Vol 8 (2) ◽  
pp. 107-122
Author(s):  
Muhammad Shareza Hafiz ◽  
Radiman Radiman ◽  
Maya Sari ◽  
Jufrizen Jufrizen
Keyword(s):  
Data Analysis ◽  
Stock Exchange ◽  
Financial Statements ◽  
Research Data ◽  
Capital Adequacy ◽  
Research Approach ◽  
Return On Assets ◽  
Analysis Techniques ◽  
Capital Adequacy Ratio ◽  
Exchange Data

This study aims to analyze the effect of Non-Performing Loans (NPLs), Capital Adequacy Ratio (CAR), and Loan to Deposit Ratio (LDR), simultaneously on Return on Assets (ROA) on BUMN Banks listed on the Indonesia Stock Exchange either partially and simultaneously. The research approach used in this study uses an associative approach. This research was conducted at the Indonesia Stock Exchange (IDX) specifically Bank BUMN listed on the Indonesia Stock Exchange (IDX). The population used in this study was state-owned Bank companies listed on the Indonesia Stock Exchange (IDX) which amounted to 4 companies. Based on the sample withdrawal criteria above, a research sample of 4 BUMN bank companies was obtained. The type of data used is documentary data, which are research data in the form of financial statements owned by state-owned banks listed on the Indonesia Stock Exchange. Data analysis techniques are used to test the effect of Non-Performing Loans (NPLs), Capital Adequacy Ratio (CAR), and Loan to Deposit Ratio (LDR) to Return on Assets (ROA) either partially or simultaneously is multiple linear regression. The results showed that partially Non Performing Loans (NPL) and Capital Adequacy Ratio (CAR) had a negative and not significant effect on Return on Assets. Partially, Loan to Deposit Ratio (LDR) has a negative and significant effect on Return on Assets. And simultaneously, Non Performing Loans, Capital Adequacy Ratio and Loan to Deposit Ratio have a significant effect on Return on Assets (ROA) at State-Owned Banks listed on the Indonesia Stock Exchange.  

scholarly journals Classic Insolvency Prediction Models Tested On Romanian Insurance Companies

2016 ◽  
Vol 12 (13) ◽  
pp. 18
Author(s):  
Alexandra Oniga
Keyword(s):  
Emerging Markets ◽  
Prediction Models ◽  
Factor Model ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Insurance Companies ◽  
Insurance Sector ◽  
Altman Model

This paper aims to analyse the applicability of classical bankruptcy prediction models for the Romanian insurance companies. Using four models, the Altman model, the Z-factor model, the Springate model and the model used to determine insolvency probability for the emerging markets we have conducted a study to see if they apply to Romanian insurance companies’ financial statements for the years between 2011 and 2013. We will present each model separately, analysing the indicators that led to the obtained results. In the end, we will combine the results to establish the applicability of these models to the Romanian insurance sector.

scholarly journals Pengaruh Profitabilitas, Kepemilikan Institusional dan Umur Perusahaan terhadap Integrated Reporting

Eksos ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 95-109
Author(s):  
Uyun Sundari ◽  
Ratno Agriyanto ◽  
Dessy Noor Farida
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Stock Exchange ◽  
Secondary Data ◽  
Institutional Ownership ◽  
Integrated Reporting ◽  
Analysis Techniques ◽  
Mining Companies

This research was conducted to determine the effect of profitability, institutional ownership and company age on integrated reporting. Type of research is quantitative with multiple linear regression data analysis techniques using the SPSS application. The data tested is secondary data. The population of this study is mining companies listed on the Indonesia Stock Exchange with the period 2016-2018. The sample uses a purposive sampling method which amounts to 48 samples. The results of this study indicate that first, profitability and institutional ownership have no effect on integrated reporting. Second, the age of the company affects the integrated reporting. Third, simultaneous profitability, institutional ownership and age of the company affect the integrated reporting.

scholarly journals Stock Liquidity and Stock Return

2019 ◽  
Vol 3 (2) ◽  
pp. 111
Author(s):  
Cynthia Eka Violita
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Coefficient Of Determination ◽  
Manufacturing Companies ◽  
Stock Liquidity ◽  
Analysis Techniques ◽  
Growth Opportunity ◽  
Positive Effect

This study aims to examine and analyze the effect of stock liquidity on stock returns on manufacturing companies listed on the Indonesia Stock Exchange. This study uses two control variables namely size and age and the study period from 2013 to 2017. The type of research used is quantitative, while the sample of this study are all manufacturing companies listed on the Indonesia Stock Exchange from 2013 to 2017. The method of determining the sample used is used is purposive sampling. The procedure of data collection in this research is the documentation method. The intended documentation method is collecting secondary data from the company's financial statements. Data analysis techniques used in this study were multiple linear regression, t test, and the coefficient of determination. The results showed that stock liquidity had a positive and significant effect on stock returns. While growth opportunity have a significant positive effect on stock returns.

Pengaruh Model Prediksi Kebangkrutan, Leverage, Audit Lag, Dan Ukuran Perusahaan Terhadap Opini Audit Going Concern

2013 ◽  
Vol 5 (1) ◽  
pp. 55-76
Author(s):  
Anastasia Paula Salean
Keyword(s):  
Prediction Model ◽  
Stock Exchange ◽  
Manufacturing Sector ◽  
Secondary Data ◽  
Bankruptcy Prediction ◽  
Annual Reports ◽  
Company Size ◽  
Financial Reports ◽  
Audit Opinion ◽  
Going Concern

The objective of this research is to examine the effect of bankruptcy prediction model, leverage, audit lag, and company size towards obtaining a going concern audit opinion.  The samples in this study are 11 companies listed in Indonesian Stock Exchange being classified as manufacturing sector in the year 2008-2011. The sample in this study determined based on purposive sampling. Data used in this study is a secondary data such as annual reports or financial reports.  The results from this study are (1) bankruptcy prediction model having no significant impact on obtaining a going concern audit opinion, (2) leverage having a significant impact on obtaining a going concern audit opinion, (3) audit lag leverage having a significant impact on obtaining a going concern audit opinion, (4) company size having no significant impact on obtaining a going concern audit opinion. Keywords: obtaining a going concern audit opinion, bankruptcy prediction model,leverage, audit lag, company size

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