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

Wen-Tsao Pan,Qiu-Yu Huang,Yu-Ning Pang,Fei-Yan Zhu,Zi-Yin Yang,Mei-Er Zhuang

doi:10.3389/fpubh.2021.675801

Wen-Tsao Pan, Qiu-Yu Huang + Show 4 more

Open Access

https://doi.org/10.3389/fpubh.2021.675801

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Abstract

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.

Highlights

With the outbreak of the COVID-19, tourism suffered huge losses, and the stock prices of tourism were influenced dramatically
This paper proposes a new Quantum Step Fruit Fly Optimization Algorithm (QSFOA) and compares its performance with Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA)
We explore the future economic sustainability of tourism driven by the digital age covering the COVID-19 era

Summary

INTRODUCTION

With the outbreak of the COVID-19, tourism suffered huge losses, and the stock prices of tourism were influenced dramatically. Wang and Zhuo [7] applied the FOA algorithm to optimize SVR parameters and combine them with support vector machines to develop a PCA-FOA-SVR stock price prediction model with high accuracy non-linear planning. Sun et al [12] proposed the Bayesian regularization method to optimize the training process of the BPNN to improve the generalization ability of the model and did empirical research about the closing price of Shanghai Stock. Several scholars seldom studied how to use the swarm intelligence algorithms to optimize the structure of BPNN for the preCOVID era These papers have not considered the NP-hard problems for the COVID-19 era. In section Empirical Analyses, the stock prediction model developed in this paper is measured by applying the stock data of two leading tourism companies in China.

RESEARCH METHOD

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT