A comparison of the ability of neural networks and logit regression models to predict levels of financial distress

@inproceedings{Zurada1997ACO,
  title={A comparison of the ability of neural networks and logit regression models to predict levels of financial distress},
  author={Jozef Maciej Zurada and Benjamin P. Foster and Terry J. Ward and Robert M. Barker},
  year={1997}
}
In this study we compared the classification accuracy rates of neural networks to those from ordinal logit models for a multi-state response variable. The results indicate that with the multi-state response variable, neural networks produce higher overall classification rates than ordinal logit models, but do not more accurately classify distressed firms. As a result, we can not clearly state that neural networks are superior to regression when predicting more than one level of financial… CONTINUE READING