Applying a probabilistic neural network to hotel bankruptcy prediction

Authors

  • Manuel Angel Fernández Gámez Universidad de Málaga. Spain
  • Angela Callejón Gil Universidad de Malaga. Spain
  • Ana José Cisneros Ruiz Universidad de Málaga. Spain

Keywords:

Hotel bankruptcy prediction, Probabilistic neural networks, Bankruptcy variables sensitivity, Spanish hotel industry

Abstract

Using a probabilistic neural network and a set of financial and non-financial variables, this study seeks to improve the ability of the existing bankruptcy prediction models in the hotel industry. Our aim is to construct a hotel bankruptcy prediction model that provides high accuracy, using information sufficiently distant from the bankruptcy situation, and which is able to determine the sensitivity of the explanatory variables. Based on a sample of Spanish hotels that went bankrupt between 2005 and 2012, empirical results indicate that using information nearer to bankruptcy (one and two years prior), the most relevant variable is EBITDA to Current Liabilities, but using information further from bankruptcy (three years prior), Return on Assets is the best predictor of bankruptcy.

Author Biographies

  • Manuel Angel Fernández Gámez, Universidad de Málaga. Spain

    Associate Professor

    Finance and Accounting Department

    Faculty of Economics

    Campus El Ejido s/n. 29071 Malaga. Spain.

  • Angela Callejón Gil, Universidad de Malaga. Spain

    Associate Professor

    Finance and Accounting Department

    Faculty of Economics

    Campus El Ejido s/n. 29071 Malaga. Spain.

  • Ana José Cisneros Ruiz, Universidad de Málaga. Spain

    Associate Professor

    Finance and Accounting Department

    Faculty of Economics

    Campus El Ejido s/n. 29071 Malaga. Spain.

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Published

31.01.2016

Issue

Section

Tourism/Hospitality: Research Papers

How to Cite

Fernández Gámez, M. A., Callejón Gil, A., & Cisneros Ruiz, A. J. (2016). Applying a probabilistic neural network to hotel bankruptcy prediction. Tourism & Management Studies, 12(1), 40-52. https://tmstudies.net/index.php/ectms/article/view/785

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