Abstract
The dynamic of success and failure in business is an open question. Since the seminal work of Altman, a considerable flow of literature has attempted to construct and test business failure models. More recently, the development of artificial intelligence has demonstrated great potential and several studies have shown that machine learning techniques achieved better performance than traditional statistical methods.
We approach the prediction of company success/failure in a supervised learning framework, by building a dataset of financial indicators of companies for which it is known if they were successful or not. Our sample is with 34 financial indicators characterizing 1642 companies
We built multiple models based on this dataset to study their prediction performance on new previously unseen data from different companies. We measure each model’s prediction ability in terms of the usual classification metrics used by the machine learning research community, namely precision, recall, f-score and accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Nunes, S., Estevão, C., Nicolau Filipe, M.: Determinant factors of competitiveness in the hotel sector: the case of Portugal”, Competitiveness Review, Vol. 28 No. 2, pp. 122-135. https://doi.org/10.1108/CR-07-2016-0043 (2018).
Mira, M. R., Moura, A., Monico, L., Breda, Z.: The Economic Dimension of Quality in Tour-ism: Perceptions of Portuguese Public Decision Makers at the Local Level. Journal of Quality Assurance in Hospitality & Tourism, v. 20, n. 3, p. 273-295 (2019).
INE - Instituto Nacional de Estatística. https://www.ine.pt/xpor-tal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=358629548&PUBLICACOESmodo=2 (2018).
Andraz, J. M., Norte, N. M., Goncalves, H. S.: Do tourism spillovers matter in regional eco-nomic analysis? An application to Portugal. Tourism Economics, 22(5), 939-963 (2016).
Costa, V., Almeida, C.: Low cost carriers and tourism destinations development. Case study of Oporto, Portugal. Tourism & Management Studies, 14(2), 7-15 (2018).
Ramires, A., Brandao, F., Sousa, A. C.: Motivation-based cluster analysis of international tourists visiting a World Heritage City: The case of Porto, Portugal. Journal of Destination Mar-keting & Management, 8, 49-60 (2018).
Soler, I. P., Gemar, G., Correia, M. B., Serra, F.: Algarve hotel price determinants: A hedonic pricing model. Tourism Management, 70, 311-321 (2019).
Altman, E. I.: Financial ratios, discriminant analysis and the prediction of corporate bank-ruptcy. The journal of finance, 23(4), 589-609 (1968).
Jones, S.: Corporate bankruptcy prediction: a high dimensional analysis. Review of Account-ing Studies, 22(3), 1366-1422 (2017).
de Llano, P., Piñeiro, C., Rodríguez, M.: Business failure prediction. A contribution to the synthesis of a theory, through comparative analysis of different prediction techniques. Estu-dios de Economía, 43(2), 163-198 (2016).
Jones, S., Wang, T.: Predicting private company failure: A multi-class analysis. Journal of International Financial Markets, Institutions and Money, 61, 161-188 (2019).
Gorzałczany, M. B., Rudziński, F.: A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Applied Soft Computing, 40, 206-220 (2016).
Antunes, F., Ribeiro, B., Pereira, F.: Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing, 60, 831-843 (2017).
Liang, D., Tsai, C. F., Dai, A. J., Eberle, W.: A novel classifier ensemble approach for financial distress prediction. Knowledge and Information Systems, 54(2), 437-462 (2018).
Chen, Hui-Ling; Yang, Bo; Wang, Gang; Liu, Jie; Xu, Xin; Wang, Su-Jing and Liu, Da-You: A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems. 24 (8), 1348-1359 (2011).
Hand, D. J.: Discrimination and classification. John Wiley. New Jersey (1981).
Alfaro, E.; García, N.; Gámez, M. and Elizondo, D.: Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45 (1), 110-122 (2008).
Heo, J., Yang, J. Y.: AdaBoost based bankruptcy forecasting of Korean construction compa-nies. Applied soft computing, 24, 494-499 (2014).
Romão, J., Guerreiro, J., Rodrigues, P. M.: Tourism growth and regional resilience: The ‘beach disease’and the consequences of the global crisis of 2007. Tourism Economics, 22(4), 699-714 (2016).
Pereira, J. M., Basto, M., da Silva, A. F.: Comparing logit model with discriminant analysis for predicting bankruptcy in Portuguese hospitality sector. European Journal of Tourism Re-search, 16, 276-280 (2017).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Brito, J.H., Pereira, J.M., da Silva, A.F., Angélico, M.J., Abreu, A., Teixeira, S. (2020). Machine Learning for Prediction of Business Company Failure in Hospitality Sector. In: Rocha, Á., Abreu, A., de Carvalho, J., Liberato, D., González, E., Liberato, P. (eds) Advances in Tourism, Technology and Smart Systems. Smart Innovation, Systems and Technologies, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-15-2024-2_28
Download citation
DOI: https://doi.org/10.1007/978-981-15-2024-2_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2023-5
Online ISBN: 978-981-15-2024-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)