Financial distress in the hotel industry: Evidence from a multidimensional performance framework
DOI:
https://doi.org/10.5937/menhottur2600013CKeywords:
financial distress, hotel industry, KRAS framework, Altman Emerging Market Score (EMS), multidimensional analysis, non-parametric methodsAbstract
Purpose – This study examines the multidimensional determinants of financial distress in joint-stock hotel companies using the KRAS framework, comprising the continuity, reputation, activity, and structure dimensions. The objective is to identify which dimensions best distinguish Altman-defined financial distress zones. Methodology – The empirical analysis is based on 97 firm-year observations from 17 joint-stock hotel companies in Serbia during 2019–2024. Financial distress is assessed using the Altman Emerging Market Score (EMS), classifying observations into distress, grey, and safe zones. Because the data are not normally distributed, differences among groups are examined using the Kruskal–Wallis test, followed by Bonferroni-adjusted Mann–Whitney U tests. Findings – The structure dimension show the strongest discriminatory ability across Altman-defined financial distress zones, with S1 emerging as the most informative indicator. The continuity and activity dimensions also show statistically significant differences, although with smaller effect sizes, whereas the reputation dimension does not demonstrate significant discriminatory power. Implications – The findings confirm the value of the KRAS framework as a multidimensional approach to assessing financial distress in the hotel sector and provide useful insights for managers, investors, and creditors in evaluating financial resilience and vulnerability.
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References
Agencija za privredne registre Republike Srbije [Agency for Business Registers of the Republic of Serbia] (2026). Financial statements database. Retrieved January 25, 2026 from https://www.apr.gov.rs
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I. (2005). An emerging market credit scoring system for corporate bonds. Emerging Markets Review, 6(4), 311–323. https://doi.org/10.1016/j.ememar.2005.09.007
Altman, E. I. (2018). A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies. Journal of Credit Risk, 14(4), 1–34. https://doi.org/10.21314/JCR.2018.243
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model. Journal of International Financial Management & Accounting, 28(2), 131–171. https://doi.org/10.1111/jifm.12053
Amara, U. S., & Fadli, F. (2024). Predicting financial distress in Indonesia’s hospitality sector: A 2019–2022 analysis. MBIA, 23(1), 74–93. https://doi.org/10.33557/mbia.v23i1.2952
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
Beaver, W. H., Correia, M., & McNichols, M. F. (2011). Financial statement analysis and the prediction of financial distress. Foundations and Trends in Accounting, 5(2), 99–173. https://doi.org/10.1561/1400000018
Boubaker, S., Cellier, A., Manita, R., & Saeed, A. (2020). Does corporate social responsibility reduce financial distress risk? Economic Modelling, 91, 835–851. https://doi.org/10.1016/j.econmod.2020.05.012
Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899–2939. https://doi.org/10.1111/j.1540-6261.2008.01416.x
Čavlin, M., Pešić, M., & Pešić, S. (2024). The impact of ratio analysis on the evaluation and management of liquidity in agro-processing companies. Ekonomika Poljoprivrede, 71(4), 1299–1312. https://doi.org/10.59267/ekoPolj24041299C
Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465–497. https://doi.org/10.1080/0963818042000216811
Conover W. J. (1999). Practical non-parametric statistics. New York, USA; John Wiley & Sons.
Crespí-Cladera, R., Martín-Oliver, A., & Pascual-Fuster, B. (2021). Financial distress in the hospitality industry during the COVID-19 disaster. Tourism Management, 85, 104301. https://doi.org/10.1016/j.tourman.2021.104301
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications Inc.
DeFond, M. L., & Hung, M. (2003). An empirical analysis of analysts’ cash-flow forecasts. Journal of Accounting and Economics, 35(1), 73–100. https://doi.org/10.1016/S0165-4101(02)00098-8
Diakomihalis, M. (2012). The accuracy of Altman’s models in predicting hotel bankruptcy. International Journal of Accounting and Financial Reporting, 2(2), 96v113. https://doi.org/10.5296/ijafr.v2i2.2367
Field, A. (2024). Discovering statistics using IBM SPSS statistics. SAGE Publications
Goh, E., Mat Roni, S., & Bannigidadmath, D. (2022). Thomas Cook (ed): Using Altman’s z-score analysis to examine predictors of financial bankruptcy in tourism and hospitality businesses. Asia Pacific Journal of Marketing and Logistics, 34(3), 475v487. https://doi.org/10.1108/APJML-02-2021-0126
Habib, A., Costa, M. D., Huang, H. J., Bhuiyan, M. B. U., & Sun, L. (2020). Determinants and consequences of financial distress: review of the empirical literature. Accounting & Finance, 60, 1023–1075. https://doi.org/10.1111/acfi.12400
Karanović, G., Karanović, B., & Štambuk, A. (2018). Financial analysis as a measure of risk management in Croatia’s hotel industry. KnE Social Sciences, 88–106. https://doi.org/10.18502/kss.v3i10.3532
Kim, H., & Gu, Z. (2006). Predicting restaurant bankruptcy: A logit model in comparison with a discriminant model. Journal of Hospitality & Tourism Research, 30(4), 474–493. https://doi.org/10.1177/1096348006290114
Kim, S.Y. (2018). Predicting hospitality financial distress with ensemble models: The case of US hotels, restaurants, and amusement and recreation. Service Business, 12, 483–503. https://doi.org/10.1007/s11628-018-0365-x
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491
Matejić, T., Knežević, S., Arsić, V. B., Obradović, T., Milojević, S., Adamović, M., … & Špiler, M. (2022). Assessing the impact of the COVID-19 crisis on hotel industry bankruptcy risk through novel forecasting models. Sustainability, 14(8), 4680. https://doi.org/10.3390/su14084680
Metaxas, T., & Romanopoulos, A. (2023). A literature review on the financial determinants of hotel default. Journal of Risk and Financial Management, 16(7), 323. https://doi.org/10.3390/jrfm16070323
Milašinović, M., Knežević, S., & Mitrović, A. (2019). Bankruptcy forecasting of hotel companies in the Republic of Serbia using Altman’s Z-score model. Hotel and Tourism Management, 7(2), 87–95. https://doi.org/10.5937/menhottur1902087M
Mitrović, A., Knežević, S., & Milašinović, M. (2021). Profitability analysis of hotel companies in the Republic of Serbia. Hotel and Tourism Management, 9(1), 121–134. https://doi.org/10.5937/menhottur2101121M
Obradović, T., Dmitrović, V., & Kuzmanović, M. (2019). Financial performance indicators: The impact of company’s lifetime and industry type. 5th IPMA SENET Project Management Conference (SENET 2019) (pp. 83–87). Atlantis Press. https://doi.org/10.2991/senet-19.2019.14
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 184–199. https://doi.org/10.1007/BF02755985
Pelaez-Verdet, A., & Loscertales-Sanchez, P. (2021). Key ratios for long-term prediction of hotel financial distress and corporate default: Survival analysis for an economic stagnation. Sustainability, 13(3), 1473. https://doi.org/10.3390/su13031473
Prdić, N. (2023). Međuzavisnost rentabilnosti i likvidnosti u cilju povećanja efikasnosti preduzeća [Interdependence of profitability and liquidity to increase company efficiency]. Ekonomist, 2(2), 41–48. https://doi.org/10.46793/EKONOMIST2.2.5
Romanopoulos, A., & Metaxas, T. (2024). Financial determinants of hotel bankruptcy in Greece: A multi-period logit estimation. The Journal of Developing Areas, 58(3), 91–111. https://doi.org/10.1353/jda.2024.a929942
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th Ed.). Pearson Education Limited.
Seretidou, D., Billios, D., & Stavropoulos, A. (2025). Integrative analysis of traditional and cash-flow financial ratios: Insights from a systematic comparative review. Risks, 13(4), 62. https://doi.org/10.3390/risks13040062
Septiani, E., & Arifah, D. A. (2026). Financial distress prediction using the Modified Altman Z-Score model: Empirical evidence from hospitality companies listed on the Indonesia Stock Exchange. International Journal of Economics and Management Research, 5(1), 583–587. https://doi.org/10.55606/ijemr.v5i1.696
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101–124. https://doi.org/10.1086/209665
Situm, M. (2023). Financial distress in the Austrian tourism industry: Hotels and restaurants analysis. European Journal of Tourism Research, 34, 3411. https://doi.org/10.54055/ejtr.v34i.2992
Song, H., & Witt, S. F. (2000). Tourism demand modelling and forecasting. (1st Ed.). Amsterdam, The Netherlands: Pergamon.
Tascón, M. T., Castro, P., & Valdunciel, L. (2024). Effects of financial restrictions on firms’ financial resilience against the COVID-19 pandemic: Evidence from the European hospitality industry. Applied Economics, 56(58), 8226–8241. https://doi.org/10.1080/00036846.2023.2289951
Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited: An overview of some recommended measures of effect size. Trends in Sport Sciences, 21(1), 19–25.
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Copyright (c) 2026 Miroslav Čavlin, Sanja Pešić, Mirko Pešić
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