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.
| Original language | English |
|---|---|
| Pages (from-to) | 159-167 |
| Number of pages | 9 |
| Journal | Information and Management |
| Volume | 24 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1993 |
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All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- Management Information Systems
- Information Systems
- Information Systems and Management
Cite this
Forecasting with neural networks. An application using bankruptcy data. / Fletcher, Desmond; Goss, Ernest.
In: Information and Management, Vol. 24, No. 3, 1993, p. 159-167.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Forecasting with neural networks. An application using bankruptcy data
AU - Fletcher, Desmond
AU - Goss, Ernest
PY - 1993
Y1 - 1993
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0000152448&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0000152448&partnerID=8YFLogxK
U2 - 10.1016/0378-7206(93)90064-Z
DO - 10.1016/0378-7206(93)90064-Z
M3 - Article
AN - SCOPUS:0000152448
VL - 24
SP - 159
EP - 167
JO - Information and Management
JF - Information and Management
SN - 0378-7206
IS - 3
ER -