Hybrid Discriminant Neural Networks for Bankruptcy Prediction and Risk Scoring

8Citations
Citations of this article
39Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Azayite, F. Z., & Achchab, S. (2016). Hybrid Discriminant Neural Networks for Bankruptcy Prediction and Risk Scoring. In Procedia Computer Science (Vol. 83, pp. 670–674). Elsevier. https://doi.org/10.1016/j.procs.2016.04.149

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free