Neural Network Simulation and the Prediction of Corporate Outcomes: Some Empirical Findings
Neural Networks (NN's) involve an innovative method of simulating and analysing complex and constantly changing systems of relationships. Originally developed to mimic the neural architecture and functioning of the human brain, NN techniques have recently been applied to many different business fields and have demonstrated a capability to solve complex problems. This paper investigates the use of NN techniques as a tool for the modelling and prediction of corporate bankruptcy and other corporate outcomes. The within and out-of-sample accuracy of trained NNs are compared with those of standard logit and multilogit techniques. The results of the study suggest that, from a pure predictive point of view, NN simulation produces a higher predictive accuracy and is more robust than conventional logit and multilogit models.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 2 (1995)
Issue (Month): 1 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/CIJB20 |
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/CIJB20|
When requesting a correction, please mention this item's handle: RePEc:taf:ijecbs:v:2:y:1995:i:1:p:31-50. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If references are entirely missing, you can add them using this form.