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

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  • Soo-Seon Park
  • Murat Hancer

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.

Suggested Citation

  • Soo-Seon Park & Murat Hancer, 2012. "A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry," Tourism Economics, , vol. 18(2), pages 311-338, April.
    • Handle: RePEc:sae:toueco:v:18:y:2012:i:2:p:311-338
    DOI: 10.5367/te.2012.0113
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    References listed on IDEAS

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    Cited by:

    1. Theodore Metaxas & Athanasios Romanopoulos, 2023. "A Literature Review on the Financial Determinants of Hotel Default," JRFM, MDPI, vol. 16(7), pages 1-19, July.
    2. Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    3. Jakub Horak & Jaromir Vrbka & Petr Suler, 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison," JRFM, MDPI, vol. 13(3), pages 1-15, March.
    4. Filipe B. Caires & Hugo Reis & Paulo M. M. Rodrigues, 2023. "Survival of the fittest: tourism exposure and firm survival," Applied Economics, Taylor & Francis Journals, vol. 55(60), pages 7150-7177, December.
    5. Jordi Moreno-Gené & Laura Sánchez-Pulido & Eduard Cristobal-Fransi & Natalia Daries, 2018. "The Economic Sustainability of Snow Tourism: The Case of Ski Resorts in Austria, France, and Italy," Sustainability, MDPI, vol. 10(9), pages 1-20, August.
    6. Falk, Martin, 2013. "A survival analysis of ski lift companies," Tourism Management, Elsevier, vol. 36(C), pages 377-390.
    7. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    8. Marko Špiler & Tijana Matejić & Snežana Knežević & Marko Milašinović & Aleksandra Mitrović & Vesna Bogojević Arsić & Tijana Obradović & Dragoljub Simonović & Vukašin Despotović & Stefan Milojević & Mi, 2022. "Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-54, December.
    9. Spyridou, Anastasia, 2019. "Evaluating Factors of Small and Medium Hospitality Enterprises Business Failure: a conceptual approach," MPRA Paper 93997, University Library of Munich, Germany.

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