Predictive potential of the bankruptcy global models in the tourism industry
The globalization process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. As a consequence, previous research has provided bankruptcy prediction models both of a global nature for certain industries or regions of the world, and models focused on certain economic activities. The tourism industry is not immune to this concern and in the previous literature there are certain bankruptcy prediction models, generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies, although in the bankruptcy prediction literature there is numerous evidence of the advantages offered by global models compared to those focused on single business activity. This study develops a global bankruptcy prediction model capable of predicting with high precision any of the activities carried out in the tourism industry. To this end, a sample of 406 Spanish companies that have developed their activity in three sectors of the tourism industry (hotels, restaurants, and travel agencies) in the period 2017-2019 has been used. This sample includes bankrupt and non-bankrupt corporations and has allowed the comparison between a global model and various focused models applying artificial neural network techniques. The results have confirmed the superiority of the global model and provide different sample selection and cost minimization solutions for bankruptcy prediction modeling in the tourism industry.
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B. N. and Csaki, F. (Eds.), Second international symposium on information theory, 267-281.
Alaminos, D., del Castillo, A., & Fernández, M. A. (2016) A Global Model for Bankruptcy Prediction. PLoS ONE, 11(11): e0166693. https://doi.org/10.1371/journal.pone.0166693.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x.
Amankwah-Amoah, J., Khan, Z., & Wood, G. (2021). COVID-19 and business failures: The paradoxes of experience, scale, and scope for theory and practice. European Management Journal, 39(2), 179-184. https://doi.org/10.1016/j.emj.2020.09.002.
Atiya, A. F. (2001). Bankruptcy Prediction for Credit Risk Using Neural Network: A Survey and New Results. IEEE Transactions on Neural Networks, 12(4), 929-935. https://doi.org/10.1109/72.935101.
Becerra-Vicario, R., Alaminos, D., Aranda, E., & Fernández-Gámez, M. A. (2020). Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry. Sustainability, 12, 5180. https://doi.org/10.3390/su12125180.
Callejón, A. M., Casado, A. M., Fernández, M. A., & Peláez, J. I. (2013) A System of Insolvency Prediction for industrial companies using a financial alternative model with neural networks. International Journal of Computational Intelligence Systems, 4, 1-13. http://dx.doi.org/10.1080/18756891.2013.754167.
Fernández-Gámez, M. A., Cisneros-Ruiz, A. J., & Callejón-Gil, A. (2016). Applying a probabilistic neural network to hotel bankruptcy prediction. Tourism & Management Studies, 12(1), 40-52. https://doi.org/10.18089/tms.2016.12104.
Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, 21(1), 25-42. https://doi.org/10.1016/S0278-4319(01)00013-05.
Gu, Z., & Gao, L. (2000). A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1), 37-49. https://doi.org/10.1177/146735840000200108.
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, 41, 190-195. https://doi.org/10.1111/j.2517-6161.1979.tb01072.x.
Hedija, V. (2019). Are Bankruptcy Models A Good Predictor Of Firm Financial Distress Of Travel Agents In The Czech Republic? Economy & Business Journal, 13(1), 87-93.
Higashide, T., Kinkyo, T., & Hamori, S. (2019). Analyzing industry-level vulnerability by predicting financial bankruptcy. Economic Inquiry, 57(4), 2017-2034. https://doi.org/10.1111/ecin.12817.
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: a tutorial. Computer, 29(3), 31-44. https://doi.org/10.1109/2.485891.
Kim, H., & Gu, Z. (2006a). A logistic regression analysis for predicting bankruptcy in the Hospitality Industry. The Journal of Hospitality Financial Management, 14(1), 17-34. https://doi.org/10.1080/10913211.2006.10653812.
Kim, H., & Gu, Z. (2006). Predicting Restaurant Bankruptcy. A Logit Model in Comparison with a Discriminant Model. Journal of Hospitality and Tourism Research, 30, 474–493. https://doi.org/10.1177/1096348006290114.
Kim, S.Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decisión tree models. Economic Modelling, 36, 354–362. https://doi.org/10.1016/j.econmod.2013.10.005.
Korol, T. (2013). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31, 22-30. http://dx.doi.org/10.1016/j.econmod.2012.11.017.
Laguillo, G., del Castillo, A., Fernández, M. A., & Becerra, R. (2019). Focused vs unfocused models for bankruptcy prediction: Empirical evidence for Spain. Contaduría y Administración, 64(2), 1-22. http://dx.doi.org/10.22201/fca.24488410e.2018.1488.
Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72, 3507-3516. http://dx.doi.org/10.1016/j.neucom.2009.02.018.
Li, H., & Sun, J. (2012). Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples–Evidence from Chinese hotel industry. Tourism Management, 33, 622-634. http://dx.doi.org/10.1016/tourman.2011.07.004.
Nuñez de Castro, L., & von Zuben, F. J. (1998). Optimized Training Techniques for Feedforward Neural Networks. Technical Report DCA-RT 03/98. Department of Computer Engineering and Industrial Automation. FEE/UNICAMP, Brasil.
Ohlson, J. A. (1980). Financial ratios and the probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131. http://dx.doi.org/10.2307/2490395.
Pacheco, L. (2015). SMEs probability of default: the case of hospitality sector. Tourims & Management Studies, 11(1), 153-159.
Park, S. M., & Hancer, M. (2012). A comparative study of logit and artificial neural networks in predicting bankruptcy in the hospitality industry. Tourism Economics, 18(2), 311-338. https://doi.org/10.5367/te.2012.0113.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. http://dx.doi.org/10.1214/aos/1176344136.
Tsai, C., Hsu, Y., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing, 24, 977-984. http://dx.doi.org/10.1016/j.asoc.2014.08.047.
Wu, C. H., Tzeng, G. H., Goo, Y. J., & Fang, W. C. (2007). A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert System with Applications, 32, 397-408. https://doi.org/10.1016/j.eswa.2005.12.008.
Young, H., & Gu, Z. (2010). Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model. International Journal of Hospitality Management, 29(1), 120-127. http://dx.doi.org/ 10.1016/j.ijhm.2009.06.007.
Zhou, L. (2013). Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge-Based Systems, 41, 16-25. http://dx.doi.org/10.1016/j.knosys.2012.12.007.
Copyright (c) 2021 Tourism & Management Studies
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.