Forecasting with Neural Networks: An Application Using Bankruptcy Data
Document Type
Article
Publication Date
3-1-1993
Department
Management and International Business
Abstract
In the business environment, Least-Squares estimation has long been the principle statistical method for forecasting a variable from available data with the logit regression model emerging as the principle methodology where the dependent variable is binary. Due to rapid hardware and software innovations, neural networks can now improve over the usual logit prediction model and provide a robust and less computationally demanding alternative to nonlinear regression methods. In this research, a back-propagation neural network methodology has been applied to a sample of bankrupt and non-bankrupt firms. Results indicate that this technique more accurately predicts bankruptcy than the logit model. The methodology represents a new paradigm in the investigation of causal relationships in data and offers promising results.
Publication Title
Information and Management
Volume
24
Issue
3
First Page
159
Last Page
167
Recommended Citation
Fletcher, D.,
Goss, E.
(1993). Forecasting with Neural Networks: An Application Using Bankruptcy Data. Information and Management, 24(3), 159-167.
Available at: https://aquila.usm.edu/fac_pubs/6405