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

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 St...

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Bibliographic Details
Published in:Frontiers in public health 2021-04, Vol.9, p.675801
Main Authors: Pan, Wen-Tsao, Huang, Qiu-Yu, Yang, Zi-Yin, Zhu, Fei-Yan, Pang, Yu-Ning, Zhuang, Mei-Er
Format: Article
Language:English
Subjects:
Algorithms
backpropagation neural network
China
COVID-19
COVID-19 era
Deep Learning
Humans
Public Health
quantum genetic algorithm
quantum particle swarm optimization algorithm
quantum step fruit fly optimization algorithm
Tourism
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Summary: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.
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2021.675801