Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry
| Autor: | David Alaminos, Rafael Becerra-Vicario, Eva Aranda, Manuel A. Fernández-Gámez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject Geography Planning and Development TJ807-830 restaurants Sample (statistics) 02 engineering and technology Management Monitoring Policy and Law TD194-195 Logistic regression Convolutional neural network Renewable energy sources 0502 economics and business 0202 electrical engineering electronic engineering information engineering Econometrics GE1-350 Quality (business) economic sustainability media_common bankruptcy prediction Environmental effects of industries and plants Renewable Energy Sustainability and the Environment logistic regression 05 social sciences Environmental sciences deep recurrent convolutional neural network Bankruptcy Bankruptcy prediction 020201 artificial intelligence & image processing Profitability index 050212 sport leisure & tourism Restaurant industry |
| Zdroj: | Sustainability Volume 12 Issue 12 Sustainability, Vol 12, Iss 5180, p 5180 (2020) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su12125180 |
| Popis: | Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008&ndash 2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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