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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 4, JULY 2001 929 Bankruptcy Prediction for Credit Risk Using Neural
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 4, JULY 2001 929
Bankruptcy Prediction for Credit Risk Using Neural
Networks: A Survey and New Results
Amir F. Atiya, Senior Member, IEEE
Abstract--The prediction of corporate bankruptcies is an
important and widely studied topic since it can have signifi-
cant impact on bank lending decisions and profitability. This
work presents two contributions. First we review the topic of
bankruptcy prediction, with emphasis on neural-network (NN)
models. Second, we develop an NN bankruptcy prediction model.
Inspired by one of the traditional credit risk models developed
by Merton, we propose novel indicators for the NN system. We
show that the use of these indicators in addition to traditional
financial ratio indicators provides a significant improvement in
the (out-of-sample) prediction accuracy (from 81.46% to 85.5%
for a three-year-ahead forecast).
Index Terms--Asset-based model, bankruptcy prediction, cor-
porate distress, corporate failure prediction, credit risk, default
prediction, financial ratios, financial statement data, multilayer
networks.

  

Source: Atiya, Amir - Computer Engineering Department, Cairo University

 

Collections: Computer Technologies and Information Sciences