Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models

Autor: Qiu Yu Huang, Yu Ning Pang, Wen Tsao Pan, Zi Yin Yang, Mei Er Zhuang, Fei Yan Zhu
Rok vydání: 2021
Předmět:
China
COVID-19 era
Coronavirus disease 2019 (COVID-19)
Computer science
02 engineering and technology
Machine learning
computer.software_genre
Swarm intelligence
Tourism
Dimension (vector space)
backpropagation neural network
0502 economics and business
0202 electrical engineering
electronic engineering
information engineering
Humans
quantum step fruit fly optimization algorithm
Stock (geology)
Original Research
quantum particle swarm optimization algorithm
050208 finance
business.industry
lcsh:Public aspects of medicine
Deep learning
05 social sciences
Public Health
Environmental and Occupational Health
COVID-19
deep learning
quantum genetic algorithm
lcsh:RA1-1270
Quantum genetic algorithm
020201 artificial intelligence & image processing
Public Health
Artificial intelligence
business
computer
Algorithms
Predictive modelling
Zdroj: Frontiers in Public Health, Vol 9 (2021)
Frontiers in Public Health
ISSN: 2296-2565
DOI: 10.3389/fpubh.2021.675801
Popis: This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.
Databáze: OpenAIRE
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