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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5180-:d:376089
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    References listed on IDEAS

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    Cited by:

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    2. Nataliya Rekova & Hanna Telnova & Oleh Kachur & Iryna Golubkova & Tomas Baležentis & Dalia Streimikiene, 2020. "Financial Sustainability Evaluation and Forecasting Using the Markov Chain: The Case of the Wine Business," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    3. Conceição Gomes & Cátia Malheiros & Filipa Campos & Luís Lima Santos, 2022. "COVID-19’s Impact on the Restaurant Industry," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    4. Marko Špiler & Tijana Matejić & Snežana Knežević & Marko Milašinović & Aleksandra Mitrović & Vesna Bogojević Arsić & Tijana Obradović & Dragoljub Simonović & Vukašin Despotović & Stefan Milojević & Mi, 2022. "Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-54, December.

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