Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic

Kesuma, Muhammad Ramadhani; Defung, Felisitas; Kusumawardani, Anisa

doi:10.47577/tssj.v25i1.4964

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most accurate prediction from the EM Z"-score model application is reached below the 1.1 zone, and, specifically, in the zone <0.5 (where the constant of the model was not used), with an 80% accuracy and 20% divergence. The study by Kesuma et al [112] examined the accuracy of the Springate and Grover models in predicting bankruptcy, as well as the effect on the stock prices of the tourism, restaurant, and hotel sector in Indonesia. The results imply that the Grover model is more accurate than the Springate model, as the second assessed that the whole sample would go bankrupt.…”

Section: Z"mentioning

confidence: 99%

Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models

et al. 2022

View full text Add to dashboard Cite

In this paper, we assess the impact of the COVID-19 crisis on the bankruptcy risk of a sample of 100 hotel companies and, consequently, on the hotel industry in the Republic of Serbia. The assessment applies to the period, 2019–2026, with the use of the data on the financial indicators for 2015–2020. Five novel structural time-series models, which have the indicators derived from Altman’s EM Z”-score model as predictors, were used, and a new conceptual framework for assessing bankruptcy risk is provided. The framework expands the applicability of credit-risk-scoring models to multiyear predictions, and it takes into account the dynamism of the transitions of the firms among Altman’s risk zones. The predictions that were obtained when the Springate and Zmijewski scores were applied along with the Altman Z”-scores demonstrate the fair applicability of the scores for the models that are introduced here. The results of the models were confirmed by 270 artificial neural networks and they were compared to the results of the classical time-series models. The crisis started to have a negative effect on bankruptcy risk in 2020, and this effect is expected to rise until 2023; currently, in 2022, the highest number of hotel companies may be headed for bankruptcy. Amelioration in the position of the companies cannot be expected before 2024; however, even in 2026, the risk of bankruptcy will remain high when compared to the pre-COVID-19 period and, thus, the surviving companies will become more fragile to any further exogenous changes. These results provide a basis for the adaption of state-supported measures and business policies in order to withstand the crisis and to ensure sustainability.

show abstract

Section: Z"mentioning

confidence: 99%

Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Some studies have dealt with the application of scoring techniques, especially Altman's models, in predicting the risk of bankruptcy in HI (Altman's models: Diakomihalis [98], Goh et al [96]; Springate and Grover models: Kesuma et al [99]). Statistical techniques have also been directly used in predicting the risk of bankruptcy in the T&T sector.…”

Section: Author(s) Outcomementioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic

Cited by 2 publications

References 7 publications

Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models

Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models

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

Contact Info

Product

Resources

About