Hybrid Discriminant Neural Networks for Bankruptcy Prediction and Risk Scoring
Determining the firm risk failure using financial statements has been one of the most interesting subjects for investors and decision makers. The discriminant variables that can be selected to predict firm health influence significantly the accuracy of the models especially if we have a missing data...
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| Published in | Procedia computer science Vol. 83; pp. 670 - 674 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2016.04.149 |
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| Abstract | Determining the firm risk failure using financial statements has been one of the most interesting subjects for investors and decision makers. The discriminant variables that can be selected to predict firm health influence significantly the accuracy of the models especially if we have a missing data available. We developed a hybrid model of neural networks to study the risk of failure of Moroccan firms taking into account the data availability and reliability. Based on a three-step analysis, this methodology combines discriminant analysis, multilayer neural network and self-organizing-maps. This hybrid model considers the firms’ behavior during three years to predict risk failure. It is qualified as a dynamic model because it adapts to financial environment and data availability. The model outperforms Discriminant analysis and gives a visual monitoring tool to supervise a firms’ portfolio. |
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| AbstractList | Determining the firm risk failure using financial statements has been one of the most interesting subjects for investors and decision makers. The discriminant variables that can be selected to predict firm health influence significantly the accuracy of the models especially if we have a missing data available. We developed a hybrid model of neural networks to study the risk of failure of Moroccan firms taking into account the data availability and reliability. Based on a three-step analysis, this methodology combines discriminant analysis, multilayer neural network and self-organizing-maps. This hybrid model considers the firms’ behavior during three years to predict risk failure. It is qualified as a dynamic model because it adapts to financial environment and data availability. The model outperforms Discriminant analysis and gives a visual monitoring tool to supervise a firms’ portfolio. |
| Author | Achchab, Said Azayite, Fatima Zahra |
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| Keywords | Bankruptcy prediction risk scoring Neural networks Self Organnizing Maps |
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| SubjectTerms | Bankruptcy prediction Neural networks risk scoring Self Organnizing Maps |
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| Title | Hybrid Discriminant Neural Networks for Bankruptcy Prediction and Risk Scoring |
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