Determinants of Tourism Stocks During the Covid-19
Evidence From the Deep Learning Models
Bibliographic Data
| ID | 22087285 |
|---|---|
| Authors | Wen-Tsao Pan (0000-0003-4482-7096, Guangdong University of Foreign Studies), Qiu-Yu Huang, Qiu Yu J Huang (0000-0001-5107-2336, Guangdong University of Foreign Studies, corresponding author), Zi-Yin Yang (Guangdong University of Foreign Studies), Fei-Yan Zhu (Guangdong University of Foreign Studies), Yu-Ning Pang (Guangdong University of Foreign Studies), Mei-Er Zhuang (0000-0001-7912-955X, Guangdong University of Foreign Studies) |
| Year | 2021 |
| Volume | 9 |
| Pages | 675801-675801 |
| Publication date | 2021-04-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.675801 |
| PMID | 33898386 |
| OpenAlex | W3154922526 |
| Language | EN |
| Citations received | 1 |
| References cited | 20 |
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
Algorithm · Artificial neural network · Deep learning · Geography · Machine learning · Mathematical optimization · Particle swarm optimization · Quantum · Swarm behaviour · Swarm intelligence · Computer Science · COVID-19 Pandemic Impacts · Diverse Aspects of Tourism Research · Mathematics · Stock Market Forecasting Methods · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |