USING NEURAL NETS TO COMBINE INFORMATION SETS IN CORPORATE BANKRUPTCY PREDICTION
Article first published online: 22 FEB 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Intelligent Systems in Accounting, Finance and Management
Volume 19, Issue 2, pages 90–101, April/June 2012
How to Cite
Peat, M. and Jones, S. (2012), USING NEURAL NETS TO COMBINE INFORMATION SETS IN CORPORATE BANKRUPTCY PREDICTION. Int. J. Intell. Syst. Acc. Fin. Mgmt., 19: 90–101. doi: 10.1002/isaf.334
- Issue published online: 4 JUN 2012
- Article first published online: 22 FEB 2012
- neural networks;
- logistic regression;
- accounting variables;
- distance to default;
- bankruptcy forecasting
We demonstrate that the use of a neural network (NN) model to combine information from corporate financial statements and equity markets provides improved predictive estimates of the probability of corporate bankruptcy. Using performance measures, based on the receiver operating characteristic curve, the forecast combinations from the NN models are demonstrated to outperform the forecasts derived from a forecast combination generated using a logistic regression approach. This result provides support for the use of forecast combinations generated from NN models in the estimation of corporate bankruptcy probabilities as it outperforms the standard approach of forming a hybrid forecasting model which includes all the explanatory variables. Copyright © 2012 John Wiley & Sons, Ltd.