Influence of COVID-19 pandemic on the tourism sector: evidence from China and United States stocks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The coronavirus disease (COVID-19) has adversely impacted the globally interconnected economy and brought the tourism sector to a temporary standstill. As such, this study aimed to investigate the spillover effect of industrial sectors by emphasizing the tourism sector. The study data was gathered from China and The United States (US) between 2019 and 2020 (pandemic period) using the Multivariate Generalised Autoregressive Conditional Heteroscedastic-Dynamic Conditional Correlation (MGARCH-DCC) and Wavelet Coherence Transform (CWT) techniques to analyse the investment holding period. Country-wise, the sectoral return volatility in China was significantly higher than the US counterpart. Additionally, the intra-sector correlation analyses demonstrated that Chinese sectors successfully mitigated the intra-sector correction in the last quarter of 2019. A short-term holding period was also suggested for investors in China while a long-term counterpart was recommended for investors in the US. Regarding the Chinese and US industrial sectors in the first quarter of 2020, it was mutually concluded that both country stocks reflected high volatility. The tourism sector was also negatively affected throughout the pandemic period (between 2019 and 2020). Essentially, this study offered practical contributions to investors, mutual fund holders, and brokers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it