Open AccessNeural Networks Versus Logit Regression Model For Predicting Financial Distress Response Variables
Author(s) -
Jozef Zurada,
Benjamin P. Foster,
Terry Ward,
Rodney Barker
Publication year - 2011
Publication title -
journal of applied business research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.149
H-Index - 22
eISSN - 2157-8834
pISSN - 0892-7626
DOI - 10.19030/jabr.v15i1.5685
Subject(s) - logistic regression , logit , variables , econometrics , variable (mathematics) , financial distress , artificial neural network , regression analysis , statistics , regression , computer science , artificial intelligence , mathematics , economics , mathematical analysis , financial system
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 superior to logistic regression models for the traditional dichotomous response variable, but are superior for the more complex financial distress response variable.