Stock Market Performance of the US Hospitality And Tourism During the Covid-19 Pandemic
Bibliographic record
Abstract
In this study, the stock market performance of the travel and leisure industry during the COVID-19 pandemic is investigated by use of the three-regime Markov switching model. The analysis employs daily data for six subsectors (airlines, gambling, hotels, leisure services, restaurants and bars, as well as travel and tourism) for the US from January 2018 to November 2021. Estimation results provide strong evidence of regime switching behavior with wide differences across subsectors during the course of the COVID-19 pandemic. A longer duration of high volatility characterizes the airline and leisure services indices. These sectors exhibit the most pronounced downturn that was not fully recovered in November 2021. In contrast, the period of high volatility in the restaurant, gaming, and hotel industries is relatively short, and stock market performance recovers almost to the general trend. Of all subsectors, restaurants and bars experience the shortest duration of high volatility, limited to the second quarter of 2020. The stock market indices for the travel and tourism industry (mainly car rentals) are also highly volatile, but this pattern was observed already before the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".