Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy In the Hospitality Industry

Abstract

Artificial Neural Networks (ANNs) have received a great deal of attention in the area of decision support system because of their outstanding ability to forecast and classify events to make a decision This study employed Artificial Neural Networks (ANNs) to predict bankruptcy among hospitality firms and compared the performance of ANNs in predicting hospitality firms' bankruptcy to the more conventional statistical logit model. From empirical results of the two methodologies, it was shown that neural network obtained a higher accuracy rate than did a logit model in an in-sample test as well as in holdout (testing) sample test. This result confirmed previous assertions made by many researchers stating the superiority of neural network over logit models in classification and prediction tasks.