Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model

Using financial variables as predictors, this study developed logistic regression and artificial neural network (ANN) models to predict business failures for Korean lodging firms. While both models demonstrated comparable Type I errors, the ANN model showed considerably lower Type II errors for both in-sample and hold-out sample predictions. This study also found that interest coverage is the most important signal of business failure for the Korean hotel industry. This ratio is directly related to the hotel's solvency, ability to service debts and productivity of profits and can thus be regarded as a survival indicator of Korean hotel firms.