FORECAST OF USING NEURAL NETWORKS IN THE TOURISM SECTOR

Miroslav Karahuta; Peter Gallo; Daniela Matušíková; Anna Šenková; Kristína Šambronská

doi:10.12955/cbup.v5.928

Miroslav Karahuta University of Prešov
Peter Gallo University of Prešov
Daniela Matušíková University of Prešov
Anna Šenková University of Prešov
Kristína Šambronská Faculty of Management, Prešov University in Prešov

DOI: https://doi.org/10.12955/cbup.v5.928

Keywords: Prediction models, financial health, neural networks, management, tourism

Abstract

The paper addresses the issue of management decision-making using artificial neural networks and their application in hotel management. Today, the development of tourism is of great importance and plays a very important role in the development of national economy. Balanced ranking and prediction model using financial and non-financial indicators with the application of artificial intelligence, allows us to reach a high level of effectivity and accuracy in evaluation of the financial and non-financial health of companies operating in this segment. This approach improves the manager’s ability to understand complex contexts and make better decisions for further development. It also brings new managerial and scientific point of view of an in-depth analysis of the performance of these facilities. It can help the development of tourism in terms of the application of modern management techniques built on scientific principles and thereby better integrate science and practice.

References

Altman, E. I. (2013). Predicting Financial Distress Of Companies: Revisiting The Z-Score And Zeta® models. V A. R. Bell, C. Brooks, & M. Prokopczuk, Handbook of Research Methods and Applications in Empirical Finance Cheltenham: Edward Elgar Publishing. 428-456. doi:10.4337/9780857936097.00027

Beerman, K. (1976). Possible Ways to Predict Capital Losses with Annual Financial Statements. University of Dusseldorf.

Council, W. T. (2016). Travel & Tourism - economic impact 2016 world. WTTC.

Hajdu, O., & Virág, M. (2001). A Hungarian model for predicting financial. Társadalom és gazdaság Közép- és Kelet-Európában / Society and Economy in Central and Eastern Europe, 28-46.

Hanne, T. (1997). Decision Support for MCDM That Is Neural Network-Based and Can Learn. In P. Clímaco (eds) Multicriteria Analysis. Berlin: Springer Berlin Heidelberg. 401-410. doi:10.1007/978-3-642-60667-0_38

Horváthová, J., & Mokrišová, M. (2014). Diagnostika výkonnosti podnikov s aplikáciou moderných metód hodnotenia finančnej výkonnosti [Diagnosing performance of businesses with the application of modern methods of evaluating financial performance]. Economics Management Innovation, 6(3), 46-60.

Ivanickova, M., Mihalcova, B., & Gallo, P. (2016). Assessment of companies’ financial health: Comparison of the selected prediction models. Actual Problems of Economics, 180(6), 383-391.

Kralicek, P., & Spal, J. (1993). Základy finančního hospodaření [Basics of financial management]. Praha: Linde.

Neumaier, I., & Neumaierová, I. (2005). Index IN 05. Sborník příspěvků mezinárodní vědecké konference „Evropské finanční systémy“. Brno: Ekonomicko-správní fakulta Masarykovy university v Brně. 143-148.

O’Halloran, R. (2015). Strategies for Decision Making. Retrieved from Hotel Business Review: http://hotelexecutive.com/business_review/2146/strategies-for-decision-making

Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 109-131. doi:10.2307/2490395

Šenková, A., & Šambronská, K. (2014). Hotelový a reštauračný manažment [Hotel and restaurant management.]. Prešov: Bookman.

Springate, G., Turgut, V., & Sands, E. (1983). Predicting Business Failures. CGA Magazine, 24-27.

Taffler, R., & Tisshaw, H. (1977). Going, going, gone – four factors which predict. Accountancy, 50-54.

Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 59-82.