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Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis

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  • Soo Y. Kim

Abstract

The objectives of this paper are firstly, to provide an optimal hotel bankruptcy prediction approach to minimize the empirical risk of misclassification and secondly, to investigate the functional characteristics of multivariate discriminant analysis, logistic, artificial neural networks (ANNs), and support vector machine (SVM) models in hotel bankruptcy prediction. The performances were evaluated not only in terms of overall classification and prediction accuracy but also in terms of relative error cost ratios. The results showed that ANN and SVM were very applicable models in bankruptcy prediction with data from Korean hotels. When jointly measuring both type I and type II errors, especially allowing for the greater costs associated with type I errors, however, ANN was more accurate with smaller estimated relative error costs than SVM. Thus, if the objective is to find the best early warning technique that performs accurately with small relative error costs, then, it will be worth considering ANN method for hotel bankruptcy prediction.

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  • Soo Y. Kim, 2008. "Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis," The Service Industries Journal, Taylor & Francis Journals, vol. 31(3), pages 441-468, December.
    • Handle: RePEc:taf:servic:v:31:y:2008:i:3:p:441-468
    DOI: 10.1080/02642060802712848
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    1. Yochanan Shachmurove, 2002. "Applying Artificial Neural Networks to Business, Economics and Finance," Penn CARESS Working Papers 5ecbb5c20d3d547f357aa1306, Penn Economics Department.
    2. Keating, Elizabeth K. & Fischer, Mary & Gordon, Teresa P. & Greenlee, Janet, 2005. "Assessing Financial Vulnerability in the Nonprofit Sector," Working Paper Series rwp05-002, Harvard University, John F. Kennedy School of Government.
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    Cited by:

    1. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
    2. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," JRFM, MDPI, vol. 13(3), pages 1-28, March.
    3. Joanna Wieprow & Agnieszka Gawlik, 2021. "The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland," Risks, MDPI, vol. 9(4), pages 1-11, April.

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