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

Soo-Seon Park and Murat Hancer

Tourism Economics, 2012, vol. 18, issue 2, 311-338

Abstract: Taking financial ratios as independent variables, this study used the framework of a neural network applied to hospitality firm bankruptcy, comparing the results to those of a logit model. Based on the empirical results of the two methodologies, the neural network obtained a higher accuracy rate than the logit model in an in-sample test. However, when tested with a holdout sample for verification, both models achieved a 100% accuracy rate. The study found that ‘total liabilities to total assets’ was a significant variable based on the results of both the t -test and logit analysis. Since hospitality firms are known for being highly leveraged, the conclusion can be drawn that extensive debt financing, when not accompanied by the competitive market value of equity, could play a pivotal role in forcing firms to file for bankruptcy.

Keywords: artificial neural networks; logit; bankruptcy; hospitality industry (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:18:y:2012:i:2:p:311-338

DOI: 10.5367/te.2012.0113

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