Tourism demand and the COVID-19 pandemic: an LSTM approach

ABSTRACT

This paper investigates the expected results of the current COVID-19 outbreak to arrivals of Chinese tourists to the USA and Australia. The growing market share of Chinese tourism and the fact that the county was the first to experience the pandemic make China a suitable proxy for predictions on global tourism. We employ data from the 2003 SARS outbreak to train a deep learning artificial neural network named Long Short Term Memory (LSTM). The neural network is calibrated for the particulars of the current pandemic. Our findings, which are cross-validated using backtesting, suggest that recovery of arrivals to pre-crisis levels can take from 6 to 12 months and this can have significant adverse effects not only on the tourism industry but also on other sectors that interact with it.

KEYWORDS:

• Coronavirus
• USA
• tourism development
• China
• deep learning
• long short term memory

JEL CLASSIFICATION:

• H12
• P46
• Z32

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 MERS: Middle East Respiratory Syndrome.

2 For a detailed discussion on backtesting strategies, please refer to Christoffersen (2010).

Additional information

Notes on contributors

Stathis Polyzos

Stathis Polyzos is Assistant Professor of Finance in Zayed University. He holds a Ph.D. (2019) in Finance from the University of the Aegean. He studied Economics at the University of Warwick (B.Sc., 2001) and Banking and Finance at the Open University of Cyprus (M.A., 2014). Additionally, he holds an M.Sc. (2007) in Computer Science from Staffordshire University. His major fields of interest are agent-based simulations, banking crises, financial stability and econometric modelling.

Aristeidis Samitas

Aristeidis Samitas is Professor of Finance in Zayed University. He studied Economics at the University of Athens (BSc, 1995) and Banking and Finance at the University of Birmingham (MSc, 1996). He also holds a Ph.D. (2001) in Finance from the University of Athens and has concluded Postdoctoral Research at City University, London (2009). His papers have been published in international journals and edited volumes, such as the Journal of Econometrics, Tourism Management Perspectives, etc.

Anastasia Ef. Spyridou

Anastasia Ef. Spyridou, is currently a PhD candidate for Gdansk University of Technology, Faculty of Management and Economics. Her research interest is on business failure models and bankruptcy issues. Her research has been published in various journals, such as Frontiers in Psychology, Sustainability among others.