Corporate Default Prediction Model: Evidence from the Indian Industrial Sector

2021 ◽  
pp. 097226292110362
Author(s):  
Shilpa Shetty H. ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Default Risk ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Binary Logistic Regression ◽  
Industrial Sector ◽  
Binary Logistic Regression Analysis ◽  
Default Prediction ◽  
Credit Default

The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.

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 Personal Credit Default Prediction Model Based on Convolution Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiang Zhou ◽  
Wenyu Zhang ◽  
Yefeng Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Roc Curve ◽  
Default Risk ◽  
Model Performance ◽  
Support Vector ◽  
Personal Credit ◽  
Default Prediction ◽  
Credit Default ◽  
Performance Evaluation Index

It has great significance for the healthy development of credit industry to control the credit default risk by using the information technology. For some traditional research about the credit default prediction model, more attention is paid to the model accuracy, while the business characteristics of the credit risk prevention are easy to be ignored. Meanwhile, to reduce the complicity of the model, the data features need be extracted manually, which will decrease the high-dimensional correlation among the analyzing data and then result in the low prediction performance of the model. So, in the paper, the CNN (convolutional neural network) is used to establish a personal credit default prediction model, and both ACC (accuracy) and AUC (the area under the ROC curve) are taken as the performance evaluation index of the model. Experimental results show the model ACC (accuracy) is above 95% and AUC (the area under the ROC curve) is above 99%, and the model performance is much better than the classical algorithm including the SVM (support vector machine), Bayes, and RF (random forest).

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 Faktor Keuangan Dan Karakteristik Pemerintah Daerah Terhadap Financial Distress

2020 ◽  
Vol 2 (3) ◽  
pp. 3160-3178
Author(s):  
Yoli Wulandari ◽  
Fefri Indra Arza
Keyword(s):  
Financial Distress ◽  
Local Governments ◽  
Binary Logistic Regression ◽  
Sampling Technique ◽  
Total Sample ◽  
Binary Logistic Regression Analysis ◽  
Financial Independence ◽  
Growth Ratio ◽  
Audit Opinion ◽  
West Sumatra

This study aims to determine the effect of Financial Factors (Effectiveness Ratios, Efficiency Ratios, And Growth Ratios) and Local Government Characteristics (Financial Independence Of Local Governments, Population, Area, And Audit Opinion) on the Financial Distress on the Districts/ Cities in West Sumatra Province in 2016-2018. The data in this study use secondary from BPK and BPS. The sampling technique uses a total sampling method with a total sample of 19 districts / cities wtih a period of time of 4 years. Analysis of the data using binary logistic regression analysis. The results of this study indicate that (1) ratio of effectiveness has a negative and not significant effect on financial distress, (2) Efficiency ratio has a positive and not significant effect on financial distress, (3) growth ratio has a positive and not significant effect on financial distress, (4) The financial independence of local governments has a negative and not significant effect on financial distress, (5) population has a negative and significant effect on financial distress, (6) Area has a positive and significant effect on financial distress, (7) Audit opinion has a negative and not significant effect on financial distress.

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 Relevansi Informasi Laporan Keuangan Accrual Basis: Kemampuan Rasio Keuangan Dalam Memprediksi Status Financial Distress Pemerintah Daerah Kabupaten Dan Kota Se- Indonesia Periode 2015-2017

2019 ◽  
Vol 1 (2) ◽  
pp. 795-813
Author(s):  
Waninda Waninda ◽  
Fefri Indra Arza
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Financial Distress ◽  
Binary Logistic Regression ◽  
City Government ◽  
Performance Ratio ◽  
Binary Logistic Regression Analysis ◽  
Regional Government ◽  
Significance Level

This study aims to predict the financial distress status of district and city governments in Indonesia. Research examines the relevance of financial statement information consisting of profitability ratio, liquidity ratio, performance ratio and capital stress ratio in predicting district and city government financial distress in Indonesia in 2015-2017. This study uses agency theory. The sampling method in this study used purposive sampling. This study consisted of 134 samples of districts / cities in Indonesia, the financial data used in the study were audited regional government financial reports, namely reports on audit results for 2015-2017. The type of data used is secondary data. The analysis used is binary logistic regression analysis. Based on the results of binary logistic regression analysis with a significance level of 5%, that of the four types of ratios measured using fourteen measurements obtained results (1) Profitability ratio as measured by profit margin ratio affects financial distress with an β coefficient of 51,548 and a significance value of 0,000 < 0.05, (2) The performance ratio measured by operating revenue to total revenue has an effect on financial distress with an β coefficient of -41.180 and a significance value of 0.015> 0.05, and a depreciation ratio influences financial distress with an β coefficient of 40.004 and a value significance of 0.004 <0.05

scholarly journals Quad test for fetal aneuploidy screening as a predictor of small-for-gestational age fetuses: a population-based study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rakchanok Boonpiam ◽  
Chanane Wanapirak ◽  
Supatra Sirichotiyakul ◽  
Ratanaporn Sekararithi ◽  
Kuntharee Traisrisilp ◽  
...  
Keyword(s):  
Gestational Age ◽  
Small For Gestational Age ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Normal Growth ◽  
Predictive Performance ◽  
Population Based ◽  
Binary Logistic Regression Analysis ◽  
Aneuploidy Screening ◽  
The Individual

Abstract Background To identify the relationship between quadruple test for aneuploidy screening (alpha-fetoprotein: AFP; free beta-human chorionic gonadotropin: b-hCG; unconjugated estriol: uE3 and inhibin-A: IHA) and fetal growth restriction and to construct predictive models for small-for-gestational-age (SGA) fetuses. Methods Women who underwent quadruple test for aneuploidy were followed-up for final outcomes. The multiples of the median (MoMs) of the four biochemical markers for the SGA group and those of normal fetuses were compared. The models for predicting SGA by the individual biomarkers and their combination were constructed using binary logistic regression analysis, and their diagnostic performances in predicting SGA were determined. Results Of 10,155 eligible pregnant women, 578 (5.7%) and 9577 (94.3%) had SGA and normal growth, respectively. High levels of AFP, b-hCG and IHA but low levels of uE3 significantly increased the risk of SGA. The constructed predictive equations had predictive performance for SGA, with areas under the receiver-operated characteristic curve of 0.724, 0.655, 0.597, 0.664 and 0.754 for AFP, b-hCG, uE3, IHA, and the combination, respectively. Conclusion The quad test for aneuploidy screening could also be used as a predictor of SGA, without extra-effort and extra-cost.

scholarly journals Individual participant data validation of the PICNICC prediction model for febrile neutropenia

2019 ◽  
Vol 105 (5) ◽  
pp. 439-445 ◽  
Author(s):  
Bob Phillips ◽  
Jessica Elizabeth Morgan ◽  
Gabrielle M Haeusler ◽  
Richard D Riley
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Infectious Complications ◽  
Cancer Therapies ◽  
Individual Participant Data ◽  
Documented Infection ◽  
Individual Participant

BackgroundRisk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy.MethodsAn individual participant data meta-analytic validation of the ‘Predicting Infectious ComplicatioNs In Children with Cancer’ (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O).ResultsThe PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau2=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI −0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully.ConclusionThis meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.

scholarly journals Group Affiliation and Default Prediction

2019 ◽  
Vol 65 (8) ◽  
pp. 3559-3584 ◽  
Author(s):  
William H. Beaver ◽  
Stefano Cascino ◽  
Maria Correia ◽  
Maureen F. McNichols
Keyword(s):  
Financial Reporting ◽  
Financial Distress ◽  
Default Risk ◽  
Credit Rating ◽  
Balance Sheet ◽  
Group Affiliation ◽  
Consolidated Financial Statements ◽  
Cross Sectional ◽  
Default Prediction ◽  
Group Information

Using a large sample of business groups from more than 100 countries around the world, we show that group information matters for parent and subsidiary default prediction. Group firms may support each other when in financial distress. Potential group support represents an off-balance sheet asset for the receiving firm and an off-balance sheet liability for the firm offering support. We find that subsidiary information improves parent default prediction over and above group-level consolidated information possibly because intragroup exposures are netted out upon consolidation. Moreover, we document that improvements in parent default prediction decrease in the extent of parent-country financial reporting transparency, a finding that suggests that within-group information matters most when consolidated financial statements are expected to be of lower quality. We also show that parent and other group-firms’ default risk exhibits predictive power for subsidiary default. Lastly, we find that within-group information explains cross-sectional variation in CDS spreads. Taken together, our findings contribute to the prior literature on default prediction and have direct relevance to investors, credit-rating agencies, and accounting regulators. This paper was accepted by Suraj Srinivasan, accounting.

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