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

Rafael Becerra-Vicario,Manuel A Fernández-Gámez,Eva Aranda,David Alaminos

doi:10.3390/su12125180

Rafael Becerra-Vicario, Manuel A Fernández-Gámez + Show 2 more

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https://doi.org/10.3390/su12125180

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Journal: Sustainability	Publication Date: Jun 25, 2020
Citations: 23	License type: CC BY 4.0

Affiliation: Universidad de Málaga

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.

Highlights

The objective of this study is to estimate bankruptcy prediction models for companies belonging to the restaurant industry
Regarding non-financial variables, we have considered the age of the restaurant (VN1), which is calculated by applying a logarithm to age
III model, in the same sense indicated by Youn and Gu (2010) and Park and Hancer (2012), who obtained neural networks (NN) models with a high level of prediction, these last authors found that, the NN models predict well, they do not provide a better prediction than logistic regression (LOGIT), especially in the external sample

Summary

Introduction

The objective of this study is to estimate bankruptcy prediction models for companies belonging to the restaurant industry. Second is due to the notable increase in bankruptcy situations of companies belonging to this sector, with a significant impact, even in the first year of activity. These circumstances have motivated interest in analyzing the causes that lead to bankruptcy in the restaurant industry and trying to provide tools or strategies to their managers, with a view to avoiding it and ensuring the permanence of their companies. This paper contributes to the literature on bankruptcy prediction in the sector: (1) provides new models with high classification accuracy, (2) uses an exclusive sample of restaurants, (3) with a horizon of up to three years for bankruptcy. We apply a novel deep learning method, Deep Recurrent Convolutional Neural Networks, which has obtained high accuracy levels in previous works [2,3]

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