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Using Neural Nets To Combine Information Sets In Corporate Bankruptcy Prediction

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  • Maurice Peat
  • Stewart Jones

Abstract

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.

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  • Maurice Peat & Stewart Jones, 2012. "Using Neural Nets To Combine Information Sets In Corporate Bankruptcy Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 90-101, April.
    • Handle: RePEc:wly:isacfm:v:19:y:2012:i:2:p:90-101
    DOI: 10.1002/isaf.334
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    References listed on IDEAS

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    4. Salim Lahmiri, 2016. "Features selection, data mining and finacial risk classification: a comparative study," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 265-275, October.

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