IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i3p1473-d490491.html
   My bibliography  Save this article

Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation

Author

Listed:
  • Antonio Pelaez-Verdet

    (Department of Business and Economics, University of Málaga, 29016 Málaga, Spain)

  • Pilar Loscertales-Sanchez

    (Department of Business and Economics, University of Málaga, 29016 Málaga, Spain)

Abstract

Hospitality companies often face economic crises, which stress their financial structure. In 2008, Spanish hotels were jeopardized when the travelers’ flows became stagnated, in either domestic and foreign markets. Most of them overcame the crisis, but not all, in part depending on their capital structure at the moment the downturn loomed upon them. This study analyzes the financial ratios registered in 2008 by 3.341 Spanish lodging enterprises, to find out the most relevant ratios that were associated with an eventual breakdown. The analyzed ratios have been largely suggested by previous literature for anticipating financial distress; however, using survival tables and Kaplan–Meier estimates we could also find new insights about several promising variates for future research. In the end, by performing a Cox regression, we could isolate the return on capital employed (ROCE) ratio as a long-term predictor for small hotels’ bankruptcy after a market downturn. Moreover, the legal status seems to be a key predictor concerning medium-sized hotels.

Suggested Citation

  • Antonio Pelaez-Verdet & Pilar Loscertales-Sanchez, 2021. "Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1473-:d:490491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/3/1473/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/3/1473/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jairaj Gupta & Mariachiara Barzotto & Amir Khorasgani, 2018. "Does size matter in predicting SMEs failure?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 571-605, October.
    2. Lili Sun, 2007. "A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction," Review of Quantitative Finance and Accounting, Springer, vol. 28(1), pages 55-78, January.
    3. Johnson, W. Bruce, 1979. "The Cross-Sectional Stability of Financial Ratio Patterns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(5), pages 1035-1048, December.
    4. Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.
    5. Voulgaris, Fotini & Doumpos, Michael & Zopounidis, Constantin, 2000. "On the Evaluation of Greek Industrial SMEs' Performance via Multicriteria Analysis of Financial Ratios," Small Business Economics, Springer, vol. 15(2), pages 127-136, September.
    6. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    7. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    8. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    9. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    10. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
    11. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    12. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    13. Li, Ming-Yuan Leon & Miu, Peter, 2010. "A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 818-833, September.
    14. 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.
    15. 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.
    16. Turetsky, Howard F & McEwen, Ruth Ann, 2001. "An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress," Review of Quantitative Finance and Accounting, Springer, vol. 16(4), pages 323-343, June.
    17. Tomczak, Sebastian, 2014. "Comparative analysis of liquidity ratios of bankrupt manufacturing companies," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 10(3), pages 1-14.
    18. Mossman, Charles E, et al, 1998. "An Empirical Comparison of Bankruptcy Models," The Financial Review, Eastern Finance Association, vol. 33(2), pages 35-53, May.
    19. 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.
    20. Tuong Le & Minh Thanh Vo & Bay Vo & Mi Young Lee & Sung Wook Baik, 2019. "A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-12, August.
    21. Edward I. Altman & Małgorzata Iwanicz-Drozdowska & Erkki K. Laitinen & Arto Suvas, 2020. "A Race for Long Horizon Bankruptcy Prediction," Applied Economics, Taylor & Francis Journals, vol. 52(37), pages 4092-4111, July.
    22. Maria Heui-Yeong Kim & Shiguang Ma & Yanran Annie Zhou, 2016. "Survival prediction of distressed firms: evidence from the Chinese special treatment firms," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 21(3), pages 418-443, July.
    23. Daniel BRÎNDESCU – OLARIU, 2016. "Profitability Ratio As A Tool For Bankruptcy Prediction," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 11, pages 369-372, July.
    24. Daniel Brîndescu Olariu, 2016. "Bankruptcy Prediction Based on the Autonomy Ratio," EuroEconomica, Danubius University of Galati, issue 2(35), pages 78-92, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Theodore Metaxas & Athanasios Romanopoulos, 2023. "A Literature Review on the Financial Determinants of Hotel Default," JRFM, MDPI, vol. 16(7), pages 1-19, July.
    2. Iftikhar Ahmad & Izlin Ismail & Shahrin Saaid Shaharuddin, 2021. "Predictive Role of Ex Ante Strategic Firm Characteristics for Sustainable Initial Public Offering (IPO) Survival," Sustainability, MDPI, vol. 13(14), pages 1-26, July.
    3. Katarina Valaskova & Tomas Kliestik & Dominika Gajdosikova, 2021. "Distinctive determinants of financial indebtedness: evidence from Slovak and Czech enterprises," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(3), pages 639-659, September.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    2. Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    3. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    4. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    5. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    6. Sami Ben Jabeur & Youssef Fahmi, 2014. "Les modèles de prévision de la défaillance des entreprises françaises : une approche comparative," Working Papers 2014-317, Department of Research, Ipag Business School.
    7. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
    8. Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    9. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
    10. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    11. Marco Muscettola, 2019. "Distinctiveness of Highly Risky Italian Firms That are Saved-A Logistic Approach," Applied Economics and Finance, Redfame publishing, vol. 6(1), pages 64-73, January.
    12. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    13. Luca Ianni & Gianluca Marullo & Stefania Migliori & Francesco De Luca, 2021. "I modelli predittivi della crisi e dell?insolvenza aziendale. Una systematic review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(2), pages 127-146.
    14. Akarsh Kainth & Ranik Raaen Wahlstrøm, 2021. "Do IFRS Promote Transparency? Evidence from the Bankruptcy Prediction of Privately Held Swedish and Norwegian Companies," JRFM, MDPI, vol. 14(3), pages 1-15, March.
    15. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    16. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    17. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    18. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," 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. 26(1), pages 294-314, Diciembre.
    19. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    20. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1473-:d:490491. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.