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Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models

Citations

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Cited by:

  1. Yang Huo & Leo H. Chan & Doug Miller, 2024. "Bankruptcy Prediction for Restaurant Firms: A Comparative Analysis of Multiple Discriminant Analysis and Logistic Regression," JRFM, MDPI, vol. 17(9), pages 1-15, September.
  2. Huaming Du & Xingyan Chen & Yu Zhao & Qing Li & Fuzhen Zhuang & Fuji Ren & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised Mar 2025.
  3. repec:agr:journl:v:4(605):y:2015:i:4(605):p:85-98 is not listed on IDEAS
  4. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
  5. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
  6. Sarbjit Singh Oberoi & Sayan Banerjee, 2023. "Bankruptcy Prediction of Indian Banks Using Advanced Analytics," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 22-41.
  7. Minwoo Lee & Yoon Koh & Araceli Hernandez & Taehyee Um, 2025. "AI-powered predictive modeling for food-service crowdfunding success: An integrated approach with business intelligence and supervised machine learning for smart business ecosystem," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-18, December.
  8. Sebastian DROZDZ & Marcus DUFWA & Robiel MECONNEN & Klaus SOLBERG SØILEN, 2015. "An Assessment of Customer Shared Value in the Restaurant Industry – a Survey from Sweden," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(605), W), pages 85-98, Winter.
  9. Marco Taboga, 2022. "Cross-country differences in the size of venture capital financing rounds: a machine learning approach," Empirical Economics, Springer, vol. 62(3), pages 991-1012, March.
  10. Jie Sun & Mengjie Zhou & Wenguo Ai & Hui Li, 2019. "Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry," Risk Management, Palgrave Macmillan, vol. 21(4), pages 215-242, December.
  11. 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.
  12. 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.
  13. Dejian Yu & Bo Xiang, 2025. "Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-19, December.
  14. Yin Shi & Xiaoni Li, 2024. "Bankruptcy prediction for the European aviation industry: An application of the Altman model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(1), pages 477-491, January.
  15. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
  16. Lijiao Yang & Yishuang Qi & Xinyu Jiang, 2021. "An Investigation of the Initial Recovery Time of Chinese Enterprises Affected by COVID-19 Using an Accelerated Failure Time Model," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
  17. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
  18. Romero Martínez, Mariano & Carmona Ibáñez, Pedro & Pozuelo Campillo, José, 2021. "Utilidad del Deep Learning en la predicción del fracaso empresarial en el ámbito europeo || The usefulness of Deep Learning in the prediction of business failure at the European level," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 392-414, December.
  19. Wanke, Peter & Barros, Carlos Pestana, 2016. "Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach," Economic Modelling, Elsevier, vol. 53(C), pages 8-22.
  20. Michaela Staňková & David Hampel, 2018. "Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(5), pages 1347-1356.
  21. Fedorova, Elena & Ledyaeva, Svetlana & Drogovoz, Pavel & Nevredinov, Alexandr, 2022. "Economic policy uncertainty and bankruptcy filings," International Review of Financial Analysis, Elsevier, vol. 82(C).
  22. du Jardin, Philippe, 2021. "Forecasting corporate failure using ensemble of self-organizing neural networks," European Journal of Operational Research, Elsevier, vol. 288(3), pages 869-885.
  23. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
  24. Sebastian Klaudiusz Tomczak & Anna Skowrońska-Szmer & Jan Jakub Szczygielski, 2020. "Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
  25. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
  26. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
  27. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
  28. Chen, Fu-Hsiang & Chi, Der-Jang & Wang, Yi-Cheng, 2015. "Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree," Economic Modelling, Elsevier, vol. 46(C), pages 1-10.
  29. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
  30. Egor O. Bukharin & Sofia I. Mangileva & Vladislav V. Afanasev, 2024. "Default Prediction for Russian Food Service Firms: Contribution of Non-Financial Factors and Machine Learning," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(1), pages 206-226.
  31. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
  32. David Alaminos & Manuel Ángel Fernández, 2019. "Why do football clubs fail financially? A financial distress prediction model for European professional football industry," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
  33. Yinghua Song & Minzhe Jiang & Shixuan Li & Shengzhe Zhao, 2024. "Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 593-614, April.
  34. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
  35. Pascal Kündig, 2025. "Alternative data for restaurant bankruptcy prediction," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 161(1), pages 1-18, December.
  36. Spyridou, Anastasia, 2019. "Evaluating Factors of Small and Medium Hospitality Enterprises Business Failure: a conceptual approach," MPRA Paper 93997, University Library of Munich, Germany.
  37. Fernández-Gámez, Manuel Ángel & Soria, Juan Antonio Campos & Santos, José António C. & Alaminos, David, 2020. "European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors," Economic Modelling, Elsevier, vol. 88(C), pages 398-407.
  38. Jin Kuang & Tse-Chen Chang & Chia-Wei Chu, 2022. "Research on Financial Early Warning Based on Combination Forecasting Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
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