Bootstrap Replacement to Validate the Influence of the Economic Cycle on the Structure and the Accuracy Level of Business Failure Prediction Models

2015 ◽  
Vol 34 (4) ◽  
pp. 275-289 ◽  
Author(s):  
Montserrat Manzaneque ◽  
Domingo GarcíA-Pérez-De-Lema ◽  
Marcos Antón Renart
Keyword(s):  
Prediction Models ◽  
Failure Prediction ◽  
Business Failure ◽  
Economic Cycle ◽  
Accuracy Level

A Review of Business Failure Prediction Models Used on Financial Distressed Companies

2017 ◽  
Vol 23 (11) ◽  
pp. 10497-10502
Author(s):  
Nur Diyana Abdullah ◽  
Sulaiman Abdul Malik
Keyword(s):  
Prediction Models ◽  
Failure Prediction ◽  
Business Failure

scholarly journals Inability of Gearing-Ratio as Predictor for Early Warning Systems

2014 ◽  
Vol 5 (2) ◽  
pp. 23-45 ◽  
Author(s):  
Mario Situm
Keyword(s):  
Early Warning ◽  
Model Building ◽  
Prediction Models ◽  
Failure Prediction ◽  
Financial Statements ◽  
Early Warning Systems ◽  
Business Failure ◽  
Warning Systems ◽  
General Applicability ◽  
Observation Period

AbstractBackground: Research in business failure and insolvency prediction provides numerous potential variables, which are in the position to differentiate between solvent and insolvent firms. Nevertheless, not all of them have the same discriminatory power, and therefore their general applicability as crisis indicators within early warning systems seems questionable. Objectives: The paper aims to demonstrate that gearing-ratio is not an appropriate predictor for firm failures/bankruptcies. Methods/Approach: The first and the second order derivatives for the gearing-ratio formula were computed and mathematically analysed. Based on these results an interpretation was given and the suitability of gearing-ratio as a discriminator within business failure prediction models was discussed. These theoretical findings were then empirically tested using financial figures from financial statements of Austrian companies for the observation period between 2008 and 2010. Results: The theoretical assumptions showed that gearing-ratio is not a suitable predictor for early warning systems. This finding was confirmed with empirical data. Conclusions: The inclusion of gearing-ratio within business failure prediction models is not able to provide early warning signals and should therefore be ignored in future model building attempts.

scholarly journals Business Failure Prediction for Slovak Small and Medium-Sized Companies

2020 ◽  
Vol 12 (11) ◽  
pp. 4572 ◽  
Author(s):  
Lucia Svabova ◽  
Lucia Michalkova ◽  
Marek Durica ◽  
Elvira Nica
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
National Economy ◽  
Prediction Models ◽  
Failure Prediction ◽  
Real Data ◽  
Business Failure ◽  
Early Prediction ◽  
Combined Model ◽  
Financial Difficulties

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.

BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES

1999 ◽  
Vol 04 (01) ◽  
Author(s):  
C. Zopounidis ◽  
M. Doumpos ◽  
R. Slowinski ◽  
R. Susmaga ◽  
A. I. Dimitras
Keyword(s):  
Multivariate Analysis ◽  
Rough Sets ◽  
Failure Prediction ◽  
Business Failure ◽  
Analysis Techniques

Statistical Failure Prediction Models for Brittle Materials.

AIAA Journal ◽  
1984 ◽  
Vol 22 (1) ◽  
pp. 135-140 ◽  
Author(s):  
Antonio C. Rufin ◽  
Dean R. Samos ◽  
R. J. H. Bollard
Keyword(s):  
Prediction Models ◽  
Brittle Materials ◽  
Failure Prediction

Financial ratio selection for business failure prediction using soft set theory

2014 ◽  
Vol 63 ◽  
pp. 59-67 ◽  
Author(s):  
Wei Xu ◽  
Zhi Xiao ◽  
Xin Dang ◽  
Daoli Yang ◽  
Xianglei Yang
Keyword(s):  
Set Theory ◽  
Failure Prediction ◽  
Soft Set ◽  
Business Failure ◽  
Financial Ratio ◽  
Selection For
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