Add to ORKGWe evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction
In business analytics and the financial world, bankruptcy prediction has been ...
Using large amounts of data from small and medium-sized industrial firms, this study examines two as...
International audienceThe use of neural networks in finance began by the end of the 1980s and by the...
We evaluate the prediction accuracy of models designed using different classification methods depend...
Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks ...
Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature,...
This paper is a critical review of the variable selection methods used to build empirical bankruptcy...
The use of multivariate discriminant analysis (MDA) and logistic regression procedure (Logit) in pr...
Recent literature strongly suggests that machine learning approaches to classification outperform "c...
This paper examined bankruptcy predictive accuracy of five statistics models--discriminant analysis ...
The article attempts to answer the question whether or not the latest bankruptcy prediction techniqu...
The prediction of corporate bankruptcies is an important and widely studied topic since it can have ...
Prediction of corporates bankruptcies is a topic that has gained more importance in the last two dec...
Recent literature strongly suggests that machine learning approaches to classification outperform "c...
Using large amounts of data from small and medium-sized industrial firms, this study examines two as...
In business analytics and the financial world, bankruptcy prediction has been ...
Using large amounts of data from small and medium-sized industrial firms, this study examines two as...
International audienceThe use of neural networks in finance began by the end of the 1980s and by the...
We evaluate the prediction accuracy of models designed using different classification methods depend...
Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks ...
Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature,...
This paper is a critical review of the variable selection methods used to build empirical bankruptcy...
The use of multivariate discriminant analysis (MDA) and logistic regression procedure (Logit) in pr...
Recent literature strongly suggests that machine learning approaches to classification outperform "c...
This paper examined bankruptcy predictive accuracy of five statistics models--discriminant analysis ...
The article attempts to answer the question whether or not the latest bankruptcy prediction techniqu...
The prediction of corporate bankruptcies is an important and widely studied topic since it can have ...
Prediction of corporates bankruptcies is a topic that has gained more importance in the last two dec...
Recent literature strongly suggests that machine learning approaches to classification outperform "c...
Using large amounts of data from small and medium-sized industrial firms, this study examines two as...
In business analytics and the financial world, bankruptcy prediction has been ...
Using large amounts of data from small and medium-sized industrial firms, this study examines two as...
International audienceThe use of neural networks in finance began by the end of the 1980s and by the...