Predicting going concern opinion for hotel industry using classifiers combination.

Manuel Ángel Fernández, José Ramón Sánchez, David Alaminos, Gisela Casado


The prediction of qualifications for going concern has been the focus of attention of the accounting and financial research with the purpose of creating models that help auditors to assess the normal course of business. Previous studies about going concern opinion prediction have been developed exclusively for manufacture and financial companies. However, there are no previous experiences of companies from the hotel industry. In the last decades, hotel industry has become one of the largest industries with the greatest expansion in the world, and this industry has its own features that we should pay special attention to. This paper provides an exclusive going concern prediction model for the hotel industry using computational methods of variable selection and classifiers combination. According to the results, companies that hold a high proportion of current assets, low return on asset margin, high leverage ratio, low current ratio, possess establishments that have a non-vacational style and don’t belong to a chain, are more likely to get a going concern opinion. The document offers a view of the challenges faced by auditors in the hotel industry and how the implementation of a proper model to foresee opinions of going concern can help auditors to cope with these challenges. 


Classifiers combination, Going concern opinion, prediction, Computational methods of variable selection, Hotel industry

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