Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks

Marko Špiler, Tijana Matejić, Snežana Knežević (), Marko Milašinović, Aleksandra Mitrović, Vesna Bogojević Arsić, Tijana Obradović, Dragoljub Simonović, Vukašin Despotović, Stefan Milojević, Miljan Adamović, Milan Resimić and Predrag Milošević
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Marko Špiler: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Tijana Matejić: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Snežana Knežević: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Marko Milašinović: Faculty of Hotel Management and Tourism in Vrnjačka Banja, University of Kragujevac, 36210 Vrnjačka Banja, Serbia
Aleksandra Mitrović: Faculty of Hotel Management and Tourism in Vrnjačka Banja, University of Kragujevac, 36210 Vrnjačka Banja, Serbia
Vesna Bogojević Arsić: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Tijana Obradović: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Dragoljub Simonović: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Vukašin Despotović: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Stefan Milojević: Audit, Accounting, Financial and Consulting Service Company “Moodys Standards” Ltd., 11000 Belgrade, Serbia
Miljan Adamović: Pharmacy Institution “Zdravlje Lek”, 11000 Belgrade, Serbia
Milan Resimić: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Predrag Milošević: Ministry of Interior of the Republic of Serbia, Bulevar Mihajla Pupina, 11000 Belgrade, Serbia

Sustainability, 2022, vol. 15, issue 1, 1-54

Abstract: In this paper we demonstrate a new conceptual framework in the application of multilayer perceptron (MLP) artificial neural networks (ANNs) to bankruptcy risk prediction using different time-delay neural network (TDNN) models to assess Altman’s EM Z″-score risk zones of firms for a sample of 100 companies operating in the hotel industry in the Republic of Serbia. Hence, the accuracies of 9580 forecasting ANNs trained for the period 2016 to 2021 are analyzed, and the impact of various input parameters of different ANN models on their forecasting accuracy is investigated, including Altman’s bankruptcy risk indicators, market and internal nonfinancial indicators, the lengths of the learning periods of the ANNs and of their input parameters, and the K-means clusters of risk zones. Based on this research, 11 stability indicators (SIs) for the years under analysis are formulated, which represent the generalization capabilities of ANN models, i.e., differences in the generalization errors between the preceding period and the year for which zone assessment is given; these are seen as a consequence of structural changes at the industry level that occurred during the relevant year. SIs are validated through comparison with the relative strength index (RSI) for descriptive indicators of Altman’s model, and high correlation is found. Special focus is placed on the identification of the stability in 2020 in order to assess the impact of the COVID-19 crisis during that year. It is established that despite the fact that the development of bankruptcy risk in the hotel industry in the Republic of Serbia is a highly volatile process, the largest changes in the analyzed period occurred in 2020, i.e., the potential applications of ANNs for forecasting zones in 2020 are limited.

Keywords: bankruptcy risk; stability; time series artificial neural networks; hotel industry; Altman’s Z-score; COVID-19 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2022:i:1:p:272-:d:1013461

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