Description, explanation, prediction – the evolution of bankruptcy studies?

Cybinski, Patricia Janina

doi:10.1108/03074350110767123

Cited by 55 publications

(32 citation statements)

References 16 publications

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“…When the distinction between survival and failure is a fine one, neural network technology can be a promising tool for solving the classification problem, i.e. the problem of classifying an entity into one of a finite collection of groups based on the attributes of that entity (Cybinski, 2001). …”

Section: Neural Networkmentioning

confidence: 99%

Comparison of Bankruptcy Prediction Models: Evidence from India

Bapat¹,

Nagale²

2014

AFR

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The purpose of this paper is to develop and compare the performance of bankruptcy prediction models using multiple discriminant analysis, logistic regression and neural network for listed companies in India. Accordingly bankruptcy prediction models are developed, over the three years prior to bankruptcy using financial ratios. The sample consists of 72 bankrupt and 72 non-bankrupt companies over the period 1991-2013. The results indicate that compared to multiple discriminant analysis and logistic regression, neural network has the highest classification accuracy for all the three years prior to bankruptcy. This study will be useful to financial institutions, investors, creditors and auditors to identify companies that are likely to experience bankruptcy.

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Section: Neural Networkmentioning

confidence: 99%

Comparison of Bankruptcy Prediction Models: Evidence from India

Bapat¹,

Nagale²

2014

AFR

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“…Later research turned to multivariate models and according to Cybinski (2001), the major difference between the early and late studies was that early studies were related to explanation rather than prediction and they concentrated on process rather than result. GAs is stochastic search methods that can search large and complex spaces on the ideas from natural genetics and evolutionary principle (Goldberg 1989;Holland 1975).…”

Section: Introductionmentioning

confidence: 98%

Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis

Bateni

Asghari

2016

Comput Econ

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One of important subjects for business and financial institutions in recent decades is bankruptcy prediction. In this study, we predict bankruptcy using both logit and genetic algorithm (GA) prediction techniques under sanctions circumstances. This study also compares the performance of bankruptcy prediction models by identifying conditions under which a model performs better to examine the relative performance of models, GA was used to classify 174 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange for the period 2006-2014. Genetic model achieved 95 and 93.5 % accuracy rates in training and test samples, respectively; while logit model achieved only 77 and 75 % accuracy rates in training and test samples, respectively. The results suggest that two models have the capability of predicting bankruptcy and GA model is more accurate than the logit model in this regard.

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“…2 Además de los trabajos citados en este artículo, un resumen de los modelos más relevantes desarrollados tanto en Estados Unidos como en otros países europeos y de todo el mundo puede encontrarse en las revisiones bibliográfi-cas realizadas por Altman (1984), Dimitras et al (1996), Cybinski (2001) ó Ravi Kumar & Ravi (2007).…”

Section: Definición De Fracaso Empresarialunclassified

Análisis del fracaso empresarial por sectores: factores diferenciadores = Cross-industry analysis of business failure: differential factors

Quintana

Gallego

Pascual

2012

Pecvnia

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ResumenEl objetivo de este trabajo se centra en el análisis del fracaso empresarial por sectores, a fin de identificar los factores explicativos y predictivos de este fenómeno que son diferentes en tres de los principales sectores que se distinguen en toda economía: industria, construcción y servicios. Para cada uno de estos sectores, seguimos el mismo procedimiento. En primer lugar, aplicamos un análisis de componentes principales con el que identificamos los factores explicativos del fracaso empresarial en los tres sectores. A continuación, consideramos dichos factores como variables independientes en un análisis discriminante, que aplicamos para predecir el fracaso de una muestra de empresas, utilizando no sólo información financiera en forma de ratios, sino también otras variables no financieras relativas a las empresas, así como información externa a las mismas que refleja las condiciones macroeconómicas bajo las que desarrollan su actividad.Palabras clave: Fracaso empresarial; Ratios financieros; Información no financiera; Análisis discriminante; Sectores. AbstractThis paper focuses on a cross-industry analysis of business failure, in order to identify the For each one of these industries, the same procedure is followed. First, a principal components analysis is applied in order to identify the explanatory factors of business failure in the three industries. Next, these factors are considered as independent variables in a discriminant analysis, so as to predict the firms' failure, using not only financial information expressed by ratios, but also other non-financial variables related to the firms, as well as external information that reflects macroeconomic conditions under which they develop their activity.Keywords: Business failure; Financial ratios; Non-financial information; Discriminant analysis; Industries. INTRODUCCIÓNLa predicción del fracaso empresarial es un importante campo de investigación dentro de la literatura financiera, que en los últimos tiempos ha adquirido gran trascendencia, debido a la severa crisis económica y financiera que está afectando a muchos países en Europa y el resto del mundo.En este contexto de incertidumbre, las empresas, cuya actividad es la base para el desarrollo de las economías, sufren tales consecuencias que les llevan a su fracaso o crisis o, incluso, a su desaparición. Esto tiene importantes efectos en los agentes con los que la empresa se relaciona, tales como accionistas, empleados, clientes y proveedores, pero también en otros negocios que actúan en el sistema económico (Lang y Stulz, 1992), lo que puede provocar un impacto general de gran magnitud en las economías. Por tanto, se hace necesario disponer de herramientas que permitan anticipar el posible fracaso de una empresa y, por consiguiente, evitar los efectos que el fracaso empresarial tiene sobre los agentes económicos que participan en el sistema.

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Description, explanation, prediction – the evolution of bankruptcy studies?

Cited by 55 publications

References 16 publications

Comparison of Bankruptcy Prediction Models: Evidence from India

Comparison of Bankruptcy Prediction Models: Evidence from India

Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis

Análisis del fracaso empresarial por sectores: factores diferenciadores = Cross-industry analysis of business failure: differential factors

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