A comparative study of logit and Artificial Neural Networks in predicting bankruptcy in the hospitality industry.

摘要

Scope and method of study. The hospitality industry has been received scrutiny by many researchers because of its unique characteristics such as fluctuating supply-demand chain, seasonality, and high level of leverage. This is why much research has been conducted to find the best tool for early warning of bankruptcy. 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.;Findings and conclusions. 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. Even though ANNs achieved the higher prediction accuracy, they do not provide the user with useful information about how the model arrives at this prediction. Therefore, it is recommended that those who utilize such predictive tools be aware