Ketepatan Altman Score, Zmijewski Score, Grover Score, dan Fulmer Score dalam menentukan Financial Distress pada Perusahaan Trade and Service

Owner ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 278
Author(s):  
Munawarah Munawarah ◽  
Anton Wijaya ◽  
Cindy Fransisca ◽  
Felicia Felicia ◽  
Kavita Kavita
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Financial Reports ◽  
Service Company ◽  
A Value

This research purpose to determine the accuracy among Altman, Zmijewski, Grover, and the Fulmer models in predicting financial distress, and to determine the most accurate prediction models to use in Trade and Service company. With the accuracy of the overall prediction model of 89.4%, this research will compare the four prediction models using real conditions of the company. The Data that used in this research are all form of annual financial reports published by companies on the Indonesia Stock Exchange website. The population used is Trade and Service’s company listed on the Indonesia Stock Exchange for the period 2013-2017. Purposive sampling used in this research to obtain 34 companies as research sample. This research compares four prediction models of financial distress using logistic regression analysis. According to the result of this research shows the accuracy between the Altman, Zmijewski, Grover, and Fulmer models to predict financial distress, which the highest level of accuracy is achieved by Zmijewski model and Fulmer model with a value of 100%, followed by Grover model with a value of 97% while Altman model with a value of 73,5%.

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 Pengaruh Likuiditas, Aktivitas, Profitabilitas dan Leverage Terhadap Financial Distress (Studi Kasus pada Perusahaan Manufaktur yang Terdaftar di Bursa Efek Indonesia Periode 2014-2018)

Jurnal Ecogen ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 134
Author(s):  
Rahmadona Amelia Fitri ◽  
Syamwil Syamwil
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Financial Distress ◽  
Stock Exchange ◽  
Simple Random Sampling ◽  
Manufacturing Companies ◽  
Significance Level ◽  
Analysis Technique ◽  
Sample Technique

The research aims to determine the effect of liquidity, activity, profitability and leverage on the financial distress of manufacturing companies listed on the Indonesia Stock Exchange in the 2014-2018 period. The population is 161 companies with 805 data. The sample technique uses purposive sampling and simple random sampling to get a sample of 64 companies with 320 data, the number of samples used is 256 valid data and in accordance with the required criteria. Data analysis technique used logistic regression analysis. Based on the results of logistic regression analysis with a significance level of 5% the results of the study concluded: 1) Liquidity, activity, profitability and leverage affect the financial distress with a significance value of 0,000 < 0.05 and independent variables can hear iden the occurrence of the dependent variable of 83,8% . 2) Liquidity does not affect the financial distress with a significance value of 0.199 > 0.05. 3) Activities affect the financial distress with a significance value of 0.041 < 0.05. 4) Profitability affects the financial distress with a significance value of 0,000 < 0.05. 5) Leverage affects the financial distres with a significance value of 0,000 < 0.05.Keywords: liquidity, activity, profitability, leverage, financial distress.

Determinants of the Success of Corporate Recovery in Financial Distressed Company

2018 ◽  
Vol 3 (2) ◽  
pp. 09-14
Author(s):  
Giriati Giriati ◽  
Mustaruddin Mustaruddin ◽  
M. Rustam
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Firm Size ◽  
Financial Distress ◽  
Stock Exchange ◽  
Jel Classification ◽  
Company Size ◽  
Simple Logistic ◽  
Positive Effect

Objective - This study aims to examine and analyze the influence of severity, free assets, company size, asset retrenchment and CEO expertise on the success of recovery companies experiencing financial distress that are listed on the Indonesian Stock Exchange (IDX). Methodology/Technique - The population used in this study are all companies listed on the Indonesian Stock Exchange between 2011 and 2016. This study uses a simple logistic regression analysis to test the hypotheses. Findings - The results indicate that free assets and CEO expertise have a significant and positive effect on the success of a company's recovery. Meanwhile, variable severity, asset retrenchment and firm size do not affect the success of the company's recovery. Type of Paper - Empirical. Keywords: Turnaround/Recovery; Δ Severity; Free Assets; Company Size; Asset Retrenchment; CEO Expertise. JEL Classification: G30, G33, G39.

scholarly journals Pengaruh Financial Distress, Pertumbuhan Perusahaan Klien, Ukuran Kap, Dan Audit Delay Terhadap Auditor Switching

2019 ◽  
Vol 1 (3) ◽  
pp. 1556-1568
Author(s):  
Faradina Zikra ◽  
Efrizal Syofyan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Financial Distress ◽  
Sampling Method ◽  
Stock Exchange ◽  
Secondary Data ◽  
Auditor Switching ◽  
Mining Companies ◽  
Company Growth

This study aims to examine the effect of financial distress, client company growth, KAP size and audit delay on auditor switching. This study is classified as comparative causal research. The population in this study are mining companies listed on the Indonesia Stock Exchange period of 2013 to 2017. By using purposive sampling method, there were 85 companies as the research samples. Auditor switchingare measured by dummy. The type of data used is secondary data obtained from www.idx.co.id and use logistic regression analysis. The results of this study indicate that financial distress, client company growth, KAP size and audit delay have no influence on auditor swtiching..

scholarly journals Pengaruh Audit Tenure dan Auditor Switching Terhadap Kualitas Audit pada Perusahaan Manufaktur Di Indonesia dengan Financial Distress Sebagai Pemoderasi

2021 ◽  
Vol 3 (1) ◽  
pp. 82-97
Author(s):  
Dede Elevendra ◽  
Nayang Helmayunita
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Financial Distress ◽  
Audit Quality ◽  
Stock Exchange ◽  
Auditor Switching

The purpose of this study was to determine the effect of audit tenure and auditor switching on audit quality with financial distress as a moderating variable. The analysis of this research used logistic regression analysis. The sample consists of companies listed on the Indonesia Stock Exchange (BEI) for the current year (2015-2019). The results showed that audit tenure and auditor switching had no effect on audit quality and financial distress was unable to moderate the effect of audit tenure and auditor switching on audit quality.

scholarly journals ANALISIS FINANCIAL DISTRESS PADA PERUSAHAAN PERTAMBANGAN BATU BARA YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2017-2018

2020 ◽  
Vol 6 (1) ◽  
pp. 36-45
Author(s):  
Reza Septian Pradana
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Coal Mining ◽  
Logistic Regression Analysis ◽  
Firm Size ◽  
Financial Distress ◽  
Stock Exchange ◽  
Financial Report ◽  
Equity Ratio ◽  
Annual Financial Report

This study aims to identify financial distress for coal mining firms registered in Indonesia Stock Exchange and analyze the factors that influent. This study uses methodological logistic regression analysis. The result of analyzing annual financial report 23 coal mining firms registered in Indonesia Stock Exchange period 2017-2018 is that 5 firms get financial distress. The result of estimation using logistic regressionshows that firm size and Debt to Equity Ratio (DER) significantly influent to the opportunity of coal mining firms gets financial distress. Thus, coal mining firms registered in Indonesia Stock Exchange period 2017-2018 need to enhance asset and use equity more than debt for firm’s funding.

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.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

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 Analisis Faktor Keuangan dan Non Keuangan yang Mempengaruhi Prediksi Peringkat Obligasi (Studi empiris pada perusahaan penerbit obligasi yang terdaftar di Bursa Efek Indonesia periode 2012-2014)

AKUNTABEL ◽  
2017 ◽  
Vol 14 (1) ◽  
pp. 69
Author(s):  
Putri Sakinah ◽  
Ardi Paminto ◽  
M. Amin Kadafi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Data Collection ◽  
Logistic Regression Analysis ◽  
Sampling Method ◽  
Stock Exchange ◽  
Current Ratio ◽  
Bond Ratings ◽  
Bond Rating ◽  
Equity Ratio

The purpose of this study is to examine the effect of liquidity as measured by Current Ratio (CR), Leverage is measured by Debt to Equity Ratio (DER), guarantee and age of bonds to bond rating listed on the Indonesia Stock Exchange. The research sample used is a bond issuer company listed on the Indonesia Stock Exchange (IDX) period of 2012 to 2014. Data collection research using purposive sampling method. Data obtained by 10 companies in the period 2012 to 2014. This study used logistic regression analysis to analyze the data. The result of this research is the liquidity proxied with Current Ratio (CR) has no significant effect on the rating of bond in Indonesia Stock Exchange. The leverage proxied by the Debt to Equity Ratio significantly influences the rating of bonds on the Indonesia Stock Exchange and the age of the bonds and the guarantee does not significantly affect the rating of bonds in the Indonesia Stock ExchangeKeywords: Bonds, bond ratings, liquidity, leverage, age of bonds, guarantees

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