Neural Networks Versus Logit Regression Model For Predicting Financial Distress Response Variables

@inproceedings{Zurada2011NeuralNV,
  title={Neural Networks Versus Logit Regression Model For Predicting Financial Distress Response Variables},
  author={Jozef Maciej Zurada and Benjamin P. Foster and Terry J. Ward and Robert M. Barker},
  year={2011}
}
Neural networks are designed to detect complex relationships among variables better than traditional statistical methods. Our study examined whether the complexity of the response measure impacts whether logistic regression or a neural network produces the highest classification accuracy for financially distressed firms. We compared results obtained from the two methods for a four state response variable and a dichotomous response variable. Our results suggest that neural networks are not… CONTINUE READING