Dealing with Corporate Distress, Repair and Reallocation

2021 ◽  
Author(s):  
Raghuran G. Rajan
Keyword(s):  
Corporate Distress

Corporate distress and lobbying: Evidence from the Stimulus Act

2014 ◽  
Vol 114 (2) ◽  
pp. 256-272 ◽  
Author(s):  
Manuel Adelino ◽  
I. Serdar Dinc
Keyword(s):  
Corporate Distress

scholarly journals Corporate distress prediction using random forest and tree net for india

2020 ◽  
Vol 10 (1) ◽  
pp. 1-11
Author(s):  
Arvind Shrivastava ◽  
Nitin Kumar ◽  
Kuldeep Kumar ◽  
Sanjeev Gupta
Keyword(s):  
Random Forest ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Data Mining Tool ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Mining Tool ◽  
Corporate Distress

The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed. This is also useful to explain the association between the tendency of firm failure and its features. The results are analyzed vis-à-vis Tree Net. Both in-sample and out of sample estimations have been performed to compare Random Forest with Tree Net, which is a cutting edge data mining tool known to provide satisfactory estimation results. An exhaustive data set comprising companies from varied sectors have been included in the analysis. It is found that Tree Net procedure provides improved classification and predictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes.

scholarly journals Business Distress Prediction Using Bayesian Logistic Model for Indian Firms

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 113 ◽  
Author(s):  
Arvind Shrivastava ◽  
Kuldeep Kumar ◽  
Nitin Kumar
Keyword(s):  
Bayesian Modeling ◽  
Logistic Model ◽  
Predictive Ability ◽  
Emerging Economy ◽  
Predictive Capability ◽  
Corporate Sector ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Distressed Firms ◽  
Corporate Distress

The objective of the study is to perform corporate distress prediction for an emerging economy, such as India, where bankruptcy details of firms are not available. Exhaustive panel dataset extracted from Capital IQ has been employed for the purpose. Foremost, the study contributes by devising novel framework to capture incipient signs of distress for Indian firms by employing a combination of firm specific parameters. The strategy not only enables enlarging the sample of distressed firms but also enables to obtain robust results. The analysis applies both standard Logistic and Bayesian modeling to predict distressed firms in Indian corporate sector. Thereby, a comparison of predictive ability of the two approaches has been carried out. Both in-sample and out of sample evaluation reveal a consistently better predictive capability employing Bayesian methodology. The study provides useful structure to indicate the early signals of failure in Indian corporate sector that is otherwise limited in literature.

Managing Corporate Distress in the Philippines: Some Policy Recommendations

1998 ◽  
Vol 98 (138) ◽  
pp. 1
Author(s):  
Charles Woodruff ◽  
G. C. Lim ◽  
◽  
Keyword(s):  
The Philippines ◽  
Policy Recommendations ◽  
Corporate Distress

ORIGINS OF CORPORATE DISTRESS IN EAST ASIA

2004 ◽  
Vol 01 (02) ◽  
pp. 261-288
Author(s):  
STIJN CLAESSENS
Keyword(s):  
East Asia ◽  
Corporate Distress

scholarly journals An Analysis of the Predictors of Financial Distress for Zimbabwe Listed Corporates

2021 ◽  
pp. 1-14
Author(s):  
Louisa Muparuri ◽  
Victor Gumbo
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Performance Indicators ◽  
Financial Distress ◽  
Stock Exchange ◽  
Binary Logistic Regression ◽  
Book Value ◽  
Future Studies ◽  
Time Series Dataset ◽  
Corporate Distress

This study brings novelty to the area of corporate distress modelling in Zimbabwe by exploring company-specific indicators of corporate distress, unlike most of the previous studies, which used financial performance indicators. Using a binary logistic regression on a time series dataset collated between 2010 and 2017, this study establishes book value, book value per share, average debt to equity and equity per share as very significant determinants of corporate distress on the Zimbabwe Stock Exchange (ZSE). Future studies incorporating artificial intelligence and a combination of both the traditional financial ratios and market-based indicators is recommended to expand the scope of the study.

scholarly journals A Business Failure Index Using Rank Transformation

2018 ◽  
Vol 11 (1) ◽  
pp. 56
Author(s):  
Marialuisa Restaino ◽  
Marco Bisogno
Keyword(s):  
Financial Distress ◽  
Default Risk ◽  
Global Financial Crisis ◽  
Business Failure ◽  
Failure Index ◽  
Financial Difficulties ◽  
Rank Transformation ◽  
The Global Financial Crisis ◽  
Italian Manufacturing Firms ◽  
Corporate Distress

The global financial crisis entails a renewed attention from financial institutions, academics, and practitioners to corporate distress analysis and its forecasting. This study aims to propose a model for predicting default risk based on a business failure index using rank transformation. The procedure suggested is able to capture firms’ financial difficulties and forecast bankruptcy through the construction of a failure index based on some relevant financial ratios. By means of the estimation of failure probability, it allows to classify and predict business distress in time to take mitigating action. This procedure is evaluated by some accuracy measures on a sample of Italian manufacturing firms, and is found to be a suitable instrument for preventing financial distress.

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