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

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.

A Literature Review on the Financial Determinants of Hotel Default

“…As another aim is to keep works that provide clear-cut hotel results, studies using broader tourism or hospitality firm samples inseparably and in an indistinguishable manner are not included (Abidin et al 2020(Abidin et al , 2021Barreda et al 2017;Laguillo et al 2019;Li et al 2017Li et al , 2019Park and Hancer 2012;Pisula 2020). Another sub-filter regarding study relevance is the exclusion of ex ante bankruptcy risk assessments that do not deal with actual bankruptcies or a measure of financial distress, where the aim is to project a future potential credit profile for hotels (Gallo et al 2018;Matejić et al 2022;Špiler et al 2022;Vivel-Búa et al 2018). Studies dealing explicitly with the going concern issue (Fernández et al 2018;Zhai et al 2015) and a recent study where the only financial metric, the initial injected capital, which can be considered a balance sheet item, was not promoted in the final model (Yuan et al 2023), are excluded too.…”

“…In terms of failures, many studies have reported high percentages of firm failures in the hospitality industry in various countries (Abidin et al 2020;Boer 1992;Chathoth et al 2006;Pisula 2020;Situm 2023;Solnet et al 2010;Westgaard and van der Wijst 2001). An increased bankruptcy risk for the hotel sector may arise after the end of the COVID-19 period, as projected recently in a few studies (Crespí-Cladera et al 2021;Matejić et al 2022;Špiler et al 2022), as also due to the current energy and cost of living crises.…”

Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence

“…According to Shetty et al [17], the 1990s ushered in a new phase in the evolution of business failure prediction models, introducing innovative methods, particularly artificial intelligence algorithms, including neural networks (Špiler et al [41]) and decision trees. These artificial intelligence-based techniques offer promising alternatives to traditional statistical models, addressing their principal shortcomings (Dong and Chen [42]).…”