scholarly journals The Accuracy of the Altman, Ohlson, Springate and Zmejewski Models in Bankruptcy Predicting Trade Sector Companies in Indonesia

2020 ◽  
Vol 3 (1) ◽  
pp. 334-347 ◽  
Author(s):  
Sri Elviani ◽  
Ramadona Simbolon ◽  
Zenni Riana ◽  
Farida Khairani ◽  
Sri Puspa Dewi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Binary Logistic Regression ◽  
Bankruptcy Prediction ◽  
Accurate Model ◽  
Analysis Technique ◽  
Trade Sector ◽  
Altman Model

Bankruptcy prediction models continue to develop both in terms of forms, models, formulas, and analysis systems. Various bankruptcy prediction studies currently conducted aim to find the most appropriate and accurate bankruptcy prediction model to be used in predicting bankruptcy. This study aims to determine the most appropriate and accurate model in predicting the bankruptcy of 53 trade sector companies in Indonesia. The analysis technique used in this study is binary logistic regression. The results of this study prove that the most appropriate and accurate model in predicting bankruptcy of trade sector companies in Indonesia is the Springate model and the Altman model

scholarly journals Failure prediction of government funded start-up firms

2017 ◽  
Vol 14 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Oliver Lukason ◽  
Kaspar Käsper
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Financial Development ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Development Patterns ◽  
Start Up

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.

scholarly journals Asset Quality as a Predictor of Rural Bank Bankruptcy in Indonesia

2021 ◽  
Vol 4 (1) ◽  
pp. 44-45
Author(s):  
Hesti Budiwati ◽  
Ainun Jariah
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Prediction Models ◽  
Predictor Variable ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Asset Quality ◽  
Productive Assets

The study aims to form a bankruptcy prediction model of rural bank in Indonesia at a time variation of 1 quarter (MP1), 2 quarters (MP2), 4 quarters (MP4), and 8 quarters (MP8) before bankruptcy. The quality of productive assets as a predictor variable consist of CEA, CEAEA, and NPL. The condition of rural bank bankrupt and non bankrupt as a dependent variable. The analytical method used is logistic regression followed by testing the model accuration. The population of this study is rural bank in Indonesia. The sample used was 241 rural banks that consist of 41 bankrupt rural banks and 200 non bankrupt rural banks. The data used are the quarterly financial statements of 2006 to 2019. The study result showed that of the four prediction models that successfully built, the 1 quarter (MP1) is the most feasible and accurate used as bankruptcy prediction model of rural banks in Indonesia that formed by CEAEA and NPL ratio. The MP1 has a classification accuracy of 93,8% at the level of modelling with cut off point of 0,29 and it has a classification accuracy of 83,93% at the level of validation with cut off point of 0,12. Based on those advantage, the MP1 was chosen as a model that able to predict the bankruptcy of rural bank in Indonesia.

scholarly journals Logit and Probit application for the prediction of bankruptcy in Slovak companies

Equilibrium ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. 775-791 ◽  
Author(s):  
Maria Kovacova ◽  
Tomas Kliestik
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Statistical Methods ◽  
Prediction Models ◽  
Slovak Republic ◽  
Relevant Literature ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Data Set ◽  
Prediction Ability

Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

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 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 Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

2018 ◽  
Vol 15 (11) ◽  
pp. 2571 ◽  
Author(s):  
Byeong Mun Heo ◽  
Keun Ho Ryu
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Blood Parameters ◽  
Binary Logistic Regression Analysis

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304–1.462)] and women (OR = 1.428, 95% CI = 1.204–1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818–2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884–2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

scholarly journals PENGARUH KEBUTUHAN BERPRESTASI DAN KARAKTERISTIK PERSONAL TERHADAP KINERJA USAHA WIRAUSAHA WANITA (Survey pada Petani Tanaman Obat Keluarga (TOGA) di Kecamatan Jumantono Kabupaten Karanganyar)

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Setyaningsih SU ◽  
Edi Wibowo
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Women Entrepreneurs ◽  
Business Performance ◽  
Binary Logistic Regression ◽  
Personal Characteristics ◽  
Binary Logistic Regression Analysis ◽  
Entrepreneurship Training ◽  
Analysis Technique ◽  
Business Experience

This study aims to analyze the effect of the needs of achievement and personal characteristics (education, age, experience, training) on business performance. This research is census research, using 63 respondents of women entrepreneurs to Toga farmers in Jumantono District, Karanganyar Regency. Data analysis technique using binary logistic regression analysis. The conclusions from the results of this study indicate that the needs of achievement and personal characteristics of business experience have a significant effect on business performance, while the variables of education, age, and entrepreneurship training have no significant effect on business performance. Based on the results of this study, the odds/probabilities of the performance of women's entrepreneurial business in Toga farmers in Jumantono District, Karanganyar Regency can be predicted by the needs of achievement and business experience. The implication of this finding is that to achieve high business performance, Toga farmers in Jumantono District, Karanganyar Regency need to increase their needs for achievement and business experience.Keywords: needs of achievement, education, age, experience, training, business performance

scholarly journals Hierarchy of Covid-19-Related Flu Symptoms According to Sex and Color or Race in Reports of Patients with Severe Acute Respiratory Syndrome in Brazil

2020 ◽  
pp. 67-78
Author(s):  
João Francisco Severo- Santos ◽  
Dimítria Dahmer Santos
Keyword(s):  
Logistic Regression ◽  
Black Women ◽  
Severe Acute Respiratory Syndrome ◽  
Prediction Model ◽  
Black Men ◽  
Muscle Pain ◽  
Socioeconomic Inequality ◽  
Racial Diversity ◽  
Binary Logistic Regression ◽  
White Men

The COVID-19 is a disease that presents a wide variety of combinations and intensities of symptoms, characteristic of a Flu Syndrome (FS), which can quickly evolve to a Severe Acute Respiratory Syndrome (SARS). The objectives of this study were to evaluate the hierarchy of symptoms of FS in patients with SARS caused by COVID-19 and to develop a prediction model for potential cases based on sex and race. Binary logistic regression modeling was used in 405,419 records selected from the database of the Ministry of Health of Brazil. It was found that men were more affected by the disease, with a 15.5% higher risk than women. They also died more, with a 13.8% and 15% higher risk for all causes and for COVID-19, respectively. The chances of more than one non-white patient dying from all causes ranged from 18.4% to 38.7% and for Covid-19 it ranged from 16.7% to 64.3% according to race. Fever, muscle pain and loss of smell or taste alternate in the first three positions of the symptom hierarchy, according to sex and race. Cough was only relevant for white men and sore throat for black men. Vomiting was only relevant for black women. The best prediction model developed encompassed seven symptoms adjusted for age, sex and race, but was able to explain only 63% of the cases of COVID-19. Possibly racial diversity, and the socioeconomic inequality associated with it, make the challenge of estimating probabilities of infection by COVID-19, based on symptoms, more complicated in Brazil than in other countries.

scholarly journals Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7741
Author(s):  
Sang Yeob Kim ◽  
Gyeong Hee Nam ◽  
Byeong Mun Heo
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Strong Association ◽  
Forced Expiratory Volume ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

Sign in / Sign up

Export Citation Format

Share Document