Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1002/isaf.334 | Int. Syst. in Accounting, Finance and Management |
Keywords | Field | DocType |
corporate financial statement,corporate bankruptcy prediction,combine information sets,nn model,standard approach,neural nets,corporate bankruptcy,forecast combination,corporate bankruptcy probability,characteristic curve,john wiley,logistic regression approach,hybrid forecasting model,neural networks,logistic regression | Econometrics,Receiver operating characteristic,Computer science,Bankruptcy prediction,Artificial intelligence,Bankruptcy,Equity (finance),Artificial neural network,Logistic regression,Machine learning | Journal |
Volume | Issue | Citations |
19 | 2 | 1 |
PageRank | References | Authors |
0.35 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maurice Peat | 1 | 2 | 2.74 |
| Stewart Jones | 2 | 1 | 0.35 |