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Bankruptcy prediction and neural networks: The contribution of variable selection methods
TLDR
In this paper, variable selection techniques developed specifically for neural networks were used to improve the prediction accuracy of the models, and they may offer a useful alternative to conventional methods for variable selection.Abstract:
Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks are among the most challenging Despite the characteristics of neural networks, most of the research done until now has not taken them into consideration for building financial failure models, nor for selecting the variables to be included in the models The aim of our research is to establish that to improve the prediction accuracy of the models, variable selection techniques developed specifically for neural networks may well offer a useful alternative to conventional methodsread more
Citations
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Bankruptcy in Indian Private Sector Banks: A Neural Network Analysis
TL;DR: In this article, the authors used neural networks to predict the bankruptcy in Indian private banks using financial ratios such as ROA, GNPA, EPS, PAT, and GNP of the country.
Threat of bankruptcy and the integrity of financial statement
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References
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Journal ArticleDOI
Financial ratios, discriminant analysis and the prediction of corporate bankruptcy
TL;DR: In this paper, a set of financial and economic ratios are investigated in a bankruptcy prediction context wherein a multiple discriminant statistical methodology is employed, and the data used in the study are limited to manufacturing corporations, where an initial sample of sixty-six firms is utilized to establish a function which best discriminates between companies in two mutually exclusive groups: bankrupt and nonbankrupt firms.
Journal ArticleDOI
Financial ratios and the probabilistic prediction of bankruptcy
TL;DR: In this paper, the authors present some empirical results of a study predicting corporate failure as evidenced by the event of bankruptcy, and the methodology is one of maximum likelihood estimation of the so-called conditional logit model, in which the data set used in this study is from the seventies (1970-76).
Journal ArticleDOI
Methodological issues related to the estimation of financial distress prediction models
TL;DR: In this paper, the authors examined conceptually and empirically two estimation biases which can result when financial distress models are estimated on non-random samples and showed that these biases can result in biased parameter and probability estimates if appropriate estimation techniques are not used.
Journal ArticleDOI
Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
Kar Yan Tam,Melody Y. Kiang +1 more
TL;DR: Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness.
Proceedings ArticleDOI
A neural network model for bankruptcy prediction
Marcus D. Odom,Ramesh Sharda +1 more
TL;DR: A comparison of the predictive abilities of both the neural network and the discriminant analysis method for bankruptcy prediction shows that neural networks might be applicable to this problem.
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