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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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    1. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    2. Maria Kovacova & Tomas Kliestik & Katarina Valaskova & Pavol Durana & Zuzana Juhaszova, 2019. "Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 10(4), pages 743-772, December.
    3. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    4. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    5. Duc Hong Vo & Binh Vo-Ninh Pham & Trung Vu-Thanh Pham & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in an Emerging Market," Documentos de Trabajo del ICAE 2019-09, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    6. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    7. Katarina Valaskova & Tomas Kliestik & Maria Kovacova, 2018. "Management of financial risks in Slovak enterprises using regression analysis," Oeconomia Copernicana, Institute of Economic Research, vol. 9(1), pages 105-121, March.
    8. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    9. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Importance of accounting and firm-specific market variables for Pakistan’s listed firms," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1545739-154, January.
    10. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    11. Lucia Svabova & Marek Durica, 2019. "Being an outlier: a company non-prosperity sign?," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 14(2), pages 359-375, June.
    12. Soo-Seon Park & Murat Hancer, 2012. "A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry," Tourism Economics, , vol. 18(2), pages 311-338, April.
    13. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    14. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
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    2. 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.
    3. 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.
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