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

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  • 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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Sebastian Tomczak, 2014. "Comparative analysis of liquidity ratios of bankrupt manufacturing companies," Business and Economic Horizons (BEH), Prague Development Center, vol. 10(3), pages 151-164, October.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    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.
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    Cited by:

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    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.

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