Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

Rafael Becerra-Vicario, David Alaminos, Eva Aranda and Manuel A. Fernández-Gámez
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Rafael Becerra-Vicario: Department of Finance and Accounting, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain
David Alaminos: Department of Economic Theory and Economic History, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain
Eva Aranda: Department of Finance and Accounting, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain
Manuel A. Fernández-Gámez: Department of Finance and Accounting, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain

Sustainability, 2020, vol. 12, issue 12, 1-15

Abstract: 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 2008–2017 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.

Keywords: bankruptcy prediction; deep recurrent convolutional neural network; economic sustainability; logistic regression; restaurants (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:12:p:5180-:d:376089

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