A Development of Hotel Bankruptcy Prediction Model on Artificial Neural Network

Abstract

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which

Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation

“…This topic lacks works focused on specific industries, whereas several authors addressed the issue [15,20]. Following their works, we limited the analysis to a particular sector and activity, because hotels are places where companies are not likely to have branches in different industries, as suggested by Pech et al or Choi and Lee [23,32]. This requirement was double-checked when retrieving the dataset using, not one, but two activity filters.…”

“…In 2012, another paper [31] focused on Greek hotels and evaluated Altman's model and its accuracy among different hotel categories, splitting it by hotel stars and Z1 to Z3 scores; according to Diakomihalis, five and three-star hotels are more likely to go bankrupt than four and two-star firms. In 2014, a neural network model [32] displayed that the group of ratios that best could classify bankrupt versus non-bankrupt hotels is (a) financial expenses to sales, (b) financial expenses to operating income, (c) owner's equity growth, (d) operating income to sales, (e) total assets turnover, (f) current assets to total assets, (g) net income and depreciation to total liabilities, and (h) quick asset to total asset.…”

A Literature Review on the Financial Determinants of Hotel Default

“…For this reason, the studies are cross-checked in the databases of Scopus and the Core Collection of Web of Science, aiming for a presence in at least one of these databases. This filter excludes three more studies (Choi and Lee 2014;Nagendrakumar et al 2020;Waikar and Fernandes 2020).…”