Applying a probabilistic neural network to hotel bankruptcy prediction
Using a probabilistic neural network and a set of financial and nonfinancial 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 inform...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Algarve, ESGHT/CINTURS
2016-01-01
|
| Series: | Tourism & Management Studies |
| Subjects: | |
| Online Access: | https://tmstudies.net/index.php/ectms/article/view/785/pdf_3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Using a probabilistic neural network and a set of financial and nonfinancial 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. |
|---|---|
| ISSN: | 2182-8466 |