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The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland

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  • Joanna Wieprow

    (The Faculty of Finance and Management, WSB University, 53-609 Wrocław, Poland)

  • Agnieszka Gawlik

    (The Faculty of Economics, WSB University, 45-372 Opole, Poland)

Abstract

The aim of this article is to use multiple discriminant analysis (MDA) and logit models to assess the risk of bankruptcy of companies in the Polish tourism sector in the crisis conditions caused by the COVID-19 pandemic. A review of the literature is used to select models appropriate to analyze the risk of bankruptcy of tourism enterprises listed on the Warsaw Stock Exchange (WSE). The data are from half-year financial statements (the first half of 2019 and 2020, respectively). The obtained results are compared with the current values of the Altman EM-score model and selected financial ratios. An analysis allowed the estimation of the risk of bankruptcy of enterprises from the tourism sector in Poland as well as the assessment of the prognostic value of these models in the tourism sector and the risk of a collapse of this market in Poland. The article fills the research gap created by the negligible use of solvency analysis of the tourism sector and constitutes the basis for estimating the risk of collapse of the tourism sector in a crisis situation.

Suggested Citation

  • Joanna Wieprow & Agnieszka Gawlik, 2021. "The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland," Risks, MDPI, vol. 9(4), pages 1-11, April.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:4:p:78-:d:537746
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    References listed on IDEAS

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    1. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
    2. Gémar, Germán & Moniche, Laura & Morales, Antonio J., 2016. "Survival analysis of the Spanish hotel industry," Tourism Management, Elsevier, vol. 54(C), pages 428-438.
    3. Soo Y. Kim, 2008. "Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis," The Service Industries Journal, Taylor & Francis Journals, vol. 31(3), pages 441-468, December.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Laitinen, Erkki K. & Laitinen, Teija, 2000. "Bankruptcy prediction: Application of the Taylor's expansion in logistic regression," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 327-349.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    7. José Miguel Rodríguez-Antón & María del Mar Alonso-Almeida, 2020. "COVID-19 Impacts and Recovery Strategies: The Case of the Hospitality Industry in Spain," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    8. Arianna Agosto & Daniel Felix Ahelegbey, 2022. "Default count-based network models for credit contagion," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(1), pages 139-152, January.
    9. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    11. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    12. Jonas Mackevičius & Ruta Šneidere & Daiva Tamulevičienė, 2018. "The waves of enterprises bankruptcy and the factors that determine them: the case of Latvia and Lithuania," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(1), pages 100-114, September.
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    3. Dorota Czerwińska-Kayzer & Joanna Florek & Ryszard Staniszewski & Dariusz Kayzer, 2021. "Application of Canonical Variate Analysis to Compare Different Groups of Food Industry Companies in Terms of Financial Liquidity and Profitability," Energies, MDPI, vol. 14(15), pages 1-16, August.
    4. Asyrofa Rahmi & Hung-Yuan Lu & Deron Liang & Dinda Novitasari & Chih-Fong Tsai, 2023. "Role of Comprehensive Income in Predicting Bankruptcy," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 689-720, August.
    5. Tijana Matejić & Snežana Knežević & Vesna Bogojević Arsić & Tijana Obradović & Stefan Milojević & Miljan Adamović & Aleksandra Mitrović & Marko Milašinović & Dragoljub Simonović & Goran Milošević & Ma, 2022. "Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models," Sustainability, MDPI, vol. 14(8), pages 1-44, April.
    6. Weng Hoe Lam & Weng Siew Lam & Kah Fai Liew & Pei Fun Lee, 2023. "Decision Analysis on the Financial Performance of Companies Using Integrated Entropy-Fuzzy TOPSIS Model," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    7. 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.
    8. Jing Wang & Yi Wang & Yinchun He & Zhangxiang Zhu, 2022. "Exploring the Factors of Rural Tourism Recovery in the Post-COVID-19 Era Based on the Grounded Theory: A Case Study of Tianxi Village in Hunan Province, China," Sustainability, MDPI, vol. 14(9), pages 1-15, April.

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